# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# pyre-strict

# This file contains dummy data for the model unit tests

from typing import Dict, List

import numpy as np
import pandas as pd
# pyre-fixme[21]: Could not find name `Timestamp` in `pandas`.
from pandas import Timestamp

AIR_FCST_LINEAR_95 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 472.9444444444443,
            1: 475.60162835249025,
            2: 478.2588122605362,
            3: 480.9159961685822,
            4: 483.57318007662815,
            5: 486.23036398467417,
            6: 488.88754789272014,
            7: 491.5447318007661,
            8: 494.20191570881207,
            9: 496.85909961685803,
            10: 499.516283524904,
            11: 502.17346743295,
            12: 504.830651340996,
            13: 507.48783524904195,
            14: 510.1450191570879,
            15: 512.8022030651339,
            16: 515.4593869731799,
            17: 518.1165708812258,
            18: 520.7737547892718,
            19: 523.4309386973177,
            20: 526.0881226053638,
            21: 528.7453065134097,
            22: 531.4024904214557,
            23: 534.0596743295017,
            24: 536.7168582375476,
            25: 539.3740421455936,
            26: 542.0312260536396,
            27: 544.6884099616856,
            28: 547.3455938697316,
            29: 550.0027777777775,
        },
        "fcst_lower": {
            0: 380.6292037661305,
            1: 383.26004701147235,
            2: 385.8905370924373,
            3: 388.52067431512216,
            4: 391.1504589893095,
            5: 393.7798914284503,
            6: 396.4089719496461,
            7: 399.0377008736321,
            8: 401.66607852475926,
            9: 404.2941052309762,
            10: 406.9217813238114,
            11: 409.54910713835505,
            12: 412.1760830132403,
            13: 414.80270929062544,
            14: 417.42898631617453,
            15: 420.0549144390392,
            16: 422.68049401183924,
            17: 425.3057253906438,
            18: 427.93060893495215,
            19: 430.555145007674,
            20: 433.1793339751107,
            21: 435.8031762069345,
            22: 438.42667207616984,
            23: 441.0498219591729,
            24: 443.6726262356114,
            25: 446.2950852884452,
            26: 448.91719950390507,
            27: 451.53896927147304,
            28: 454.1603949838614,
            29: 456.78147703699216,
        },
        "fcst_upper": {
            0: 565.2596851227581,
            1: 567.9432096935082,
            2: 570.6270874286351,
            3: 573.3113180220422,
            4: 575.9959011639468,
            5: 578.680836540898,
            6: 581.3661238357942,
            7: 584.0517627279,
            8: 586.7377528928648,
            9: 589.4240940027398,
            10: 592.1107857259966,
            11: 594.797827727545,
            12: 597.4852196687516,
            13: 600.1729612074585,
            14: 602.8610519980012,
            15: 605.5494916912286,
            16: 608.2382799345206,
            17: 610.9274163718079,
            18: 613.6169006435915,
            19: 616.3067323869615,
            20: 618.9969112356168,
            21: 621.6874368198849,
            22: 624.3783087667415,
            23: 627.0695266998305,
            24: 629.7610902394838,
            25: 632.4529990027421,
            26: 635.145252603374,
            27: 637.8378506518982,
            28: 640.5307927556019,
            29: 643.2240785185628,
        },
    }
)

AIR_FCST_LINEAR_99 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 472.9444444444443,
            1: 475.60162835249025,
            2: 478.2588122605362,
            3: 480.9159961685822,
            4: 483.57318007662815,
            5: 486.23036398467417,
            6: 488.88754789272014,
            7: 491.5447318007661,
            8: 494.20191570881207,
            9: 496.85909961685803,
            10: 499.516283524904,
            11: 502.17346743295,
            12: 504.830651340996,
            13: 507.48783524904195,
            14: 510.1450191570879,
            15: 512.8022030651339,
            16: 515.4593869731799,
            17: 518.1165708812258,
            18: 520.7737547892718,
            19: 523.4309386973177,
            20: 526.0881226053638,
            21: 528.7453065134097,
            22: 531.4024904214557,
            23: 534.0596743295017,
            24: 536.7168582375476,
            25: 539.3740421455936,
            26: 542.0312260536396,
            27: 544.6884099616856,
            28: 547.3455938697316,
            29: 550.0027777777775,
        },
        "fcst_lower": {
            0: 351.01805478037915,
            1: 353.64044896268456,
            2: 356.2623766991775,
            3: 358.883838394139,
            4: 361.50483445671773,
            5: 364.12536530090745,
            6: 366.74543134552374,
            7: 369.3650330141812,
            8: 371.98417073526997,
            9: 374.6028449419319,
            10: 377.2210560720369,
            11: 379.83880456815905,
            12: 382.45609087755207,
            13: 385.07291545212513,
            14: 387.68927874841813,
            15: 390.3051812275768,
            16: 392.92062335532785,
            17: 395.5356056019535,
            18: 398.15012844226646,
            19: 400.764192355584,
            20: 403.37779782570226,
            21: 405.99094534087044,
            22: 408.60363539376465,
            23: 411.2158684814615,
            24: 413.82764510541136,
            25: 416.4389657714128,
            26: 419.04983098958445,
            27: 421.66024127433906,
            28: 424.2701971443558,
            29: 426.8796991225531,
        },
        "fcst_upper": {
            0: 594.8708341085095,
            1: 597.562807742296,
            2: 600.255247821895,
            3: 602.9481539430253,
            4: 605.6415256965386,
            5: 608.3353626684409,
            6: 611.0296644399166,
            7: 613.724430587351,
            8: 616.4196606823541,
            9: 619.1153542917842,
            10: 621.8115109777711,
            11: 624.508130297741,
            12: 627.2052118044398,
            13: 629.9027550459588,
            14: 632.6007595657577,
            15: 635.299224902691,
            16: 637.998150591032,
            17: 640.6975361604982,
            18: 643.3973811362772,
            19: 646.0976850390515,
            20: 648.7984473850253,
            21: 651.4996676859489,
            22: 654.2013454491467,
            23: 656.903480177542,
            24: 659.6060713696838,
            25: 662.3091185197744,
            26: 665.0126211176946,
            27: 667.716578649032,
            28: 670.4209905951075,
            29: 673.1258564330019,
        },
    }
)

PEYTON_FCST_LINEAR_95 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 8.479624727157459,
            1: 8.479984673362159,
            2: 8.480344619566859,
            3: 8.48070456577156,
            4: 8.48106451197626,
            5: 8.48142445818096,
            6: 8.481784404385662,
            7: 8.482144350590362,
            8: 8.482504296795062,
            9: 8.482864242999762,
            10: 8.483224189204464,
            11: 8.483584135409163,
            12: 8.483944081613863,
            13: 8.484304027818565,
            14: 8.484663974023265,
            15: 8.485023920227965,
            16: 8.485383866432667,
            17: 8.485743812637367,
            18: 8.486103758842066,
            19: 8.486463705046766,
            20: 8.486823651251468,
            21: 8.487183597456168,
            22: 8.487543543660868,
            23: 8.48790348986557,
            24: 8.48826343607027,
            25: 8.48862338227497,
            26: 8.48898332847967,
            27: 8.489343274684371,
            28: 8.489703220889071,
            29: 8.490063167093771,
        },
        "fcst_lower": {
            0: 7.055970485245664,
            1: 7.056266316358524,
            2: 7.056561800026597,
            3: 7.056856936297079,
            4: 7.057151725217398,
            5: 7.05744616683524,
            6: 7.057740261198534,
            7: 7.058034008355445,
            8: 7.058327408354395,
            9: 7.058620461244044,
            10: 7.0589131670733005,
            11: 7.059205525891312,
            12: 7.059497537747475,
            13: 7.059789202691431,
            14: 7.0600805207730595,
            15: 7.060371492042489,
            16: 7.060662116550093,
            17: 7.060952394346479,
            18: 7.06124232548251,
            19: 7.0615319100092835,
            20: 7.061821147978145,
            21: 7.062110039440677,
            22: 7.062398584448709,
            23: 7.062686783054313,
            24: 7.0629746353098,
            25: 7.063262141267724,
            26: 7.063549300980883,
            27: 7.063836114502315,
            28: 7.0641225818852975,
            29: 7.064408703183352,
        },
        "fcst_upper": {
            0: 9.903278969069254,
            1: 9.903703030365794,
            2: 9.90412743910712,
            3: 9.904552195246042,
            4: 9.904977298735123,
            5: 9.90540274952668,
            6: 9.90582854757279,
            7: 9.906254692825279,
            8: 9.90668118523573,
            9: 9.90710802475548,
            10: 9.907535211335626,
            11: 9.907962744927016,
            12: 9.908390625480251,
            13: 9.9088188529457,
            14: 9.90924742727347,
            15: 9.909676348413441,
            16: 9.91010561631524,
            17: 9.910535230928254,
            18: 9.910965192201623,
            19: 9.91139550008425,
            20: 9.91182615452479,
            21: 9.912257155471659,
            22: 9.912688502873028,
            23: 9.913120196676825,
            24: 9.91355223683074,
            25: 9.913984623282214,
            26: 9.914417355978456,
            27: 9.914850434866427,
            28: 9.915283859892844,
            29: 9.91571763100419,
        },
    }
)

PEYTON_FCST_LINEAR_99 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 8.479624727157459,
            1: 8.479984673362159,
            2: 8.480344619566859,
            3: 8.48070456577156,
            4: 8.48106451197626,
            5: 8.48142445818096,
            6: 8.481784404385662,
            7: 8.482144350590362,
            8: 8.482504296795062,
            9: 8.482864242999762,
            10: 8.483224189204464,
            11: 8.483584135409163,
            12: 8.483944081613863,
            13: 8.484304027818565,
            14: 8.484663974023265,
            15: 8.485023920227965,
            16: 8.485383866432667,
            17: 8.485743812637367,
            18: 8.486103758842066,
            19: 8.486463705046766,
            20: 8.486823651251468,
            21: 8.487183597456168,
            22: 8.487543543660868,
            23: 8.48790348986557,
            24: 8.48826343607027,
            25: 8.48862338227497,
            26: 8.48898332847967,
            27: 8.489343274684371,
            28: 8.489703220889071,
            29: 8.490063167093771,
        },
        "fcst_lower": {
            0: 6.605000045325637,
            1: 6.605275566724015,
            2: 6.605550630617649,
            3: 6.605825237068679,
            4: 6.606099386139563,
            5: 6.60637307789309,
            6: 6.606646312392368,
            7: 6.606919089700827,
            8: 6.607191409882221,
            9: 6.607463273000626,
            10: 6.607734679120443,
            11: 6.608005628306389,
            12: 6.608276120623508,
            13: 6.608546156137163,
            14: 6.608815734913038,
            15: 6.609084857017139,
            16: 6.609353522515795,
            17: 6.609621731475649,
            18: 6.609889483963668,
            19: 6.610156780047143,
            20: 6.61042361979368,
            21: 6.610690003271204,
            22: 6.610955930547961,
            23: 6.611221401692519,
            24: 6.611486416773756,
            25: 6.611750975860878,
            26: 6.612015079023405,
            27: 6.612278726331177,
            28: 6.612541917854348,
            29: 6.612804653663393,
        },
        "fcst_upper": {
            0: 10.354249408989281,
            1: 10.354693780000304,
            2: 10.355138608516068,
            3: 10.355583894474442,
            4: 10.356029637812957,
            5: 10.35647583846883,
            6: 10.356922496378955,
            7: 10.357369611479896,
            8: 10.357817183707903,
            9: 10.358265212998898,
            10: 10.358713699288483,
            11: 10.359162642511938,
            12: 10.359612042604219,
            13: 10.360061899499968,
            14: 10.360512213133493,
            15: 10.36096298343879,
            16: 10.361414210349539,
            17: 10.361865893799084,
            18: 10.362318033720465,
            19: 10.36277063004639,
            20: 10.363223682709256,
            21: 10.363677191641132,
            22: 10.364131156773775,
            23: 10.364585578038621,
            24: 10.365040455366783,
            25: 10.365495788689062,
            26: 10.365951577935935,
            27: 10.366407823037564,
            28: 10.366864523923793,
            29: 10.36732168052415,
        },
    }
)

PEYTON_FCST_LINEAR_INVALID_ZERO = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2012-05-02 00:00:00"),
            1: pd.Timestamp("2012-05-03 00:00:00"),
            2: pd.Timestamp("2012-05-04 00:00:00"),
            3: pd.Timestamp("2012-05-05 00:00:00"),
            4: pd.Timestamp("2012-05-06 00:00:00"),
            5: pd.Timestamp("2012-05-07 00:00:00"),
            6: pd.Timestamp("2012-05-08 00:00:00"),
            7: pd.Timestamp("2012-05-09 00:00:00"),
            8: pd.Timestamp("2012-05-10 00:00:00"),
            9: pd.Timestamp("2012-05-11 00:00:00"),
            10: pd.Timestamp("2012-05-12 00:00:00"),
            11: pd.Timestamp("2012-05-13 00:00:00"),
            12: pd.Timestamp("2012-05-14 00:00:00"),
            13: pd.Timestamp("2012-05-15 00:00:00"),
            14: pd.Timestamp("2012-05-16 00:00:00"),
            15: pd.Timestamp("2012-05-17 00:00:00"),
            16: pd.Timestamp("2012-05-18 00:00:00"),
            17: pd.Timestamp("2012-05-19 00:00:00"),
            18: pd.Timestamp("2012-05-20 00:00:00"),
            19: pd.Timestamp("2012-05-21 00:00:00"),
            20: pd.Timestamp("2012-05-22 00:00:00"),
            21: pd.Timestamp("2012-05-23 00:00:00"),
            22: pd.Timestamp("2012-05-24 00:00:00"),
            23: pd.Timestamp("2012-05-25 00:00:00"),
            24: pd.Timestamp("2012-05-26 00:00:00"),
            25: pd.Timestamp("2012-05-27 00:00:00"),
            26: pd.Timestamp("2012-05-28 00:00:00"),
            27: pd.Timestamp("2012-05-29 00:00:00"),
            28: pd.Timestamp("2012-05-30 00:00:00"),
            29: pd.Timestamp("2012-05-31 00:00:00"),
            30: pd.Timestamp("2012-06-01 00:00:00"),
            31: pd.Timestamp("2012-06-02 00:00:00"),
            32: pd.Timestamp("2012-06-03 00:00:00"),
            33: pd.Timestamp("2012-06-04 00:00:00"),
            34: pd.Timestamp("2012-06-05 00:00:00"),
            35: pd.Timestamp("2012-06-06 00:00:00"),
            36: pd.Timestamp("2012-06-07 00:00:00"),
            37: pd.Timestamp("2012-06-08 00:00:00"),
            38: pd.Timestamp("2012-06-09 00:00:00"),
            39: pd.Timestamp("2012-06-10 00:00:00"),
            40: pd.Timestamp("2012-06-11 00:00:00"),
            41: pd.Timestamp("2012-06-12 00:00:00"),
            42: pd.Timestamp("2012-06-13 00:00:00"),
            43: pd.Timestamp("2012-06-14 00:00:00"),
            44: pd.Timestamp("2012-06-15 00:00:00"),
            45: pd.Timestamp("2012-06-16 00:00:00"),
            46: pd.Timestamp("2012-06-17 00:00:00"),
            47: pd.Timestamp("2012-06-18 00:00:00"),
            48: pd.Timestamp("2012-06-19 00:00:00"),
            49: pd.Timestamp("2012-06-20 00:00:00"),
            50: pd.Timestamp("2012-06-21 00:00:00"),
            51: pd.Timestamp("2012-06-22 00:00:00"),
            52: pd.Timestamp("2012-06-23 00:00:00"),
            53: pd.Timestamp("2012-06-24 00:00:00"),
            54: pd.Timestamp("2012-06-25 00:00:00"),
            55: pd.Timestamp("2012-06-26 00:00:00"),
            56: pd.Timestamp("2012-06-27 00:00:00"),
            57: pd.Timestamp("2012-06-28 00:00:00"),
            58: pd.Timestamp("2012-06-29 00:00:00"),
            59: pd.Timestamp("2012-06-30 00:00:00"),
            60: pd.Timestamp("2012-07-01 00:00:00"),
            61: pd.Timestamp("2012-07-02 00:00:00"),
            62: pd.Timestamp("2012-07-03 00:00:00"),
            63: pd.Timestamp("2012-07-04 00:00:00"),
            64: pd.Timestamp("2012-07-05 00:00:00"),
            65: pd.Timestamp("2012-07-06 00:00:00"),
            66: pd.Timestamp("2012-07-07 00:00:00"),
            67: pd.Timestamp("2012-07-08 00:00:00"),
            68: pd.Timestamp("2012-07-09 00:00:00"),
            69: pd.Timestamp("2012-07-10 00:00:00"),
            70: pd.Timestamp("2012-07-11 00:00:00"),
            71: pd.Timestamp("2012-07-12 00:00:00"),
            72: pd.Timestamp("2012-07-13 00:00:00"),
            73: pd.Timestamp("2012-07-14 00:00:00"),
            74: pd.Timestamp("2012-07-15 00:00:00"),
            75: pd.Timestamp("2012-07-16 00:00:00"),
            76: pd.Timestamp("2012-07-17 00:00:00"),
            77: pd.Timestamp("2012-07-18 00:00:00"),
            78: pd.Timestamp("2012-07-19 00:00:00"),
            79: pd.Timestamp("2012-07-20 00:00:00"),
            80: pd.Timestamp("2012-07-21 00:00:00"),
            81: pd.Timestamp("2012-07-22 00:00:00"),
            82: pd.Timestamp("2012-07-23 00:00:00"),
            83: pd.Timestamp("2012-07-24 00:00:00"),
            84: pd.Timestamp("2012-07-25 00:00:00"),
            85: pd.Timestamp("2012-07-26 00:00:00"),
            86: pd.Timestamp("2012-07-27 00:00:00"),
            87: pd.Timestamp("2012-07-28 00:00:00"),
            88: pd.Timestamp("2012-07-29 00:00:00"),
            89: pd.Timestamp("2012-07-30 00:00:00"),
            90: pd.Timestamp("2012-07-31 00:00:00"),
            91: pd.Timestamp("2012-08-01 00:00:00"),
            92: pd.Timestamp("2012-08-02 00:00:00"),
            93: pd.Timestamp("2012-08-03 00:00:00"),
            94: pd.Timestamp("2012-08-04 00:00:00"),
            95: pd.Timestamp("2012-08-05 00:00:00"),
            96: pd.Timestamp("2012-08-06 00:00:00"),
            97: pd.Timestamp("2012-08-07 00:00:00"),
            98: pd.Timestamp("2012-08-08 00:00:00"),
            99: pd.Timestamp("2012-08-09 00:00:00"),
            100: pd.Timestamp("2012-08-10 00:00:00"),
            101: pd.Timestamp("2012-08-11 00:00:00"),
            102: pd.Timestamp("2012-08-12 00:00:00"),
            103: pd.Timestamp("2012-08-13 00:00:00"),
            104: pd.Timestamp("2012-08-14 00:00:00"),
            105: pd.Timestamp("2012-08-15 00:00:00"),
            106: pd.Timestamp("2012-08-16 00:00:00"),
            107: pd.Timestamp("2012-08-17 00:00:00"),
            108: pd.Timestamp("2012-08-18 00:00:00"),
            109: pd.Timestamp("2012-08-19 00:00:00"),
            110: pd.Timestamp("2012-08-20 00:00:00"),
            111: pd.Timestamp("2012-08-21 00:00:00"),
            112: pd.Timestamp("2012-08-22 00:00:00"),
            113: pd.Timestamp("2012-08-23 00:00:00"),
            114: pd.Timestamp("2012-08-24 00:00:00"),
            115: pd.Timestamp("2012-08-25 00:00:00"),
            116: pd.Timestamp("2012-08-26 00:00:00"),
            117: pd.Timestamp("2012-08-27 00:00:00"),
            118: pd.Timestamp("2012-08-28 00:00:00"),
            119: pd.Timestamp("2012-08-29 00:00:00"),
            120: pd.Timestamp("2012-08-30 00:00:00"),
            121: pd.Timestamp("2012-08-31 00:00:00"),
            122: pd.Timestamp("2012-09-01 00:00:00"),
            123: pd.Timestamp("2012-09-02 00:00:00"),
            124: pd.Timestamp("2012-09-03 00:00:00"),
            125: pd.Timestamp("2012-09-04 00:00:00"),
            126: pd.Timestamp("2012-09-05 00:00:00"),
            127: pd.Timestamp("2012-09-06 00:00:00"),
            128: pd.Timestamp("2012-09-07 00:00:00"),
            129: pd.Timestamp("2012-09-08 00:00:00"),
            130: pd.Timestamp("2012-09-09 00:00:00"),
            131: pd.Timestamp("2012-09-10 00:00:00"),
            132: pd.Timestamp("2012-09-11 00:00:00"),
            133: pd.Timestamp("2012-09-12 00:00:00"),
            134: pd.Timestamp("2012-09-13 00:00:00"),
            135: pd.Timestamp("2012-09-14 00:00:00"),
            136: pd.Timestamp("2012-09-15 00:00:00"),
            137: pd.Timestamp("2012-09-16 00:00:00"),
            138: pd.Timestamp("2012-09-17 00:00:00"),
            139: pd.Timestamp("2012-09-18 00:00:00"),
            140: pd.Timestamp("2012-09-19 00:00:00"),
            141: pd.Timestamp("2012-09-20 00:00:00"),
            142: pd.Timestamp("2012-09-21 00:00:00"),
            143: pd.Timestamp("2012-09-22 00:00:00"),
            144: pd.Timestamp("2012-09-23 00:00:00"),
            145: pd.Timestamp("2012-09-24 00:00:00"),
            146: pd.Timestamp("2012-09-25 00:00:00"),
            147: pd.Timestamp("2012-09-26 00:00:00"),
            148: pd.Timestamp("2012-09-27 00:00:00"),
            149: pd.Timestamp("2012-09-28 00:00:00"),
            150: pd.Timestamp("2012-09-29 00:00:00"),
            151: pd.Timestamp("2012-09-30 00:00:00"),
            152: pd.Timestamp("2012-10-01 00:00:00"),
            153: pd.Timestamp("2012-10-02 00:00:00"),
            154: pd.Timestamp("2012-10-03 00:00:00"),
            155: pd.Timestamp("2012-10-04 00:00:00"),
            156: pd.Timestamp("2012-10-05 00:00:00"),
            157: pd.Timestamp("2012-10-06 00:00:00"),
            158: pd.Timestamp("2012-10-07 00:00:00"),
            159: pd.Timestamp("2012-10-08 00:00:00"),
            160: pd.Timestamp("2012-10-09 00:00:00"),
            161: pd.Timestamp("2012-10-10 00:00:00"),
            162: pd.Timestamp("2012-10-11 00:00:00"),
            163: pd.Timestamp("2012-10-12 00:00:00"),
            164: pd.Timestamp("2012-10-13 00:00:00"),
            165: pd.Timestamp("2012-10-14 00:00:00"),
            166: pd.Timestamp("2012-10-15 00:00:00"),
            167: pd.Timestamp("2012-10-16 00:00:00"),
            168: pd.Timestamp("2012-10-17 00:00:00"),
            169: pd.Timestamp("2012-10-18 00:00:00"),
            170: pd.Timestamp("2012-10-19 00:00:00"),
            171: pd.Timestamp("2012-10-20 00:00:00"),
            172: pd.Timestamp("2012-10-21 00:00:00"),
            173: pd.Timestamp("2012-10-22 00:00:00"),
            174: pd.Timestamp("2012-10-23 00:00:00"),
            175: pd.Timestamp("2012-10-24 00:00:00"),
            176: pd.Timestamp("2012-10-25 00:00:00"),
            177: pd.Timestamp("2012-10-26 00:00:00"),
            178: pd.Timestamp("2012-10-27 00:00:00"),
            179: pd.Timestamp("2012-10-28 00:00:00"),
            180: pd.Timestamp("2012-10-29 00:00:00"),
            181: pd.Timestamp("2012-10-30 00:00:00"),
            182: pd.Timestamp("2012-10-31 00:00:00"),
            183: pd.Timestamp("2012-11-01 00:00:00"),
            184: pd.Timestamp("2012-11-02 00:00:00"),
            185: pd.Timestamp("2012-11-03 00:00:00"),
            186: pd.Timestamp("2012-11-04 00:00:00"),
            187: pd.Timestamp("2012-11-05 00:00:00"),
            188: pd.Timestamp("2012-11-06 00:00:00"),
            189: pd.Timestamp("2012-11-07 00:00:00"),
            190: pd.Timestamp("2012-11-08 00:00:00"),
            191: pd.Timestamp("2012-11-09 00:00:00"),
            192: pd.Timestamp("2012-11-10 00:00:00"),
            193: pd.Timestamp("2012-11-11 00:00:00"),
            194: pd.Timestamp("2012-11-12 00:00:00"),
            195: pd.Timestamp("2012-11-13 00:00:00"),
            196: pd.Timestamp("2012-11-14 00:00:00"),
            197: pd.Timestamp("2012-11-15 00:00:00"),
            198: pd.Timestamp("2012-11-16 00:00:00"),
            199: pd.Timestamp("2012-11-17 00:00:00"),
            200: pd.Timestamp("2012-11-18 00:00:00"),
            201: pd.Timestamp("2012-11-19 00:00:00"),
            202: pd.Timestamp("2012-11-20 00:00:00"),
            203: pd.Timestamp("2012-11-21 00:00:00"),
            204: pd.Timestamp("2012-11-22 00:00:00"),
            205: pd.Timestamp("2012-11-23 00:00:00"),
            206: pd.Timestamp("2012-11-24 00:00:00"),
            207: pd.Timestamp("2012-11-25 00:00:00"),
            208: pd.Timestamp("2012-11-26 00:00:00"),
            209: pd.Timestamp("2012-11-27 00:00:00"),
            210: pd.Timestamp("2012-11-28 00:00:00"),
            211: pd.Timestamp("2012-11-29 00:00:00"),
            212: pd.Timestamp("2012-11-30 00:00:00"),
            213: pd.Timestamp("2012-12-01 00:00:00"),
            214: pd.Timestamp("2012-12-02 00:00:00"),
            215: pd.Timestamp("2012-12-03 00:00:00"),
            216: pd.Timestamp("2012-12-04 00:00:00"),
            217: pd.Timestamp("2012-12-05 00:00:00"),
            218: pd.Timestamp("2012-12-06 00:00:00"),
            219: pd.Timestamp("2012-12-07 00:00:00"),
            220: pd.Timestamp("2012-12-08 00:00:00"),
            221: pd.Timestamp("2012-12-09 00:00:00"),
            222: pd.Timestamp("2012-12-10 00:00:00"),
            223: pd.Timestamp("2012-12-11 00:00:00"),
            224: pd.Timestamp("2012-12-12 00:00:00"),
            225: pd.Timestamp("2012-12-13 00:00:00"),
            226: pd.Timestamp("2012-12-14 00:00:00"),
            227: pd.Timestamp("2012-12-15 00:00:00"),
            228: pd.Timestamp("2012-12-16 00:00:00"),
            229: pd.Timestamp("2012-12-17 00:00:00"),
            230: pd.Timestamp("2012-12-18 00:00:00"),
            231: pd.Timestamp("2012-12-19 00:00:00"),
            232: pd.Timestamp("2012-12-20 00:00:00"),
            233: pd.Timestamp("2012-12-21 00:00:00"),
            234: pd.Timestamp("2012-12-22 00:00:00"),
            235: pd.Timestamp("2012-12-23 00:00:00"),
            236: pd.Timestamp("2012-12-24 00:00:00"),
            237: pd.Timestamp("2012-12-25 00:00:00"),
            238: pd.Timestamp("2012-12-26 00:00:00"),
            239: pd.Timestamp("2012-12-27 00:00:00"),
            240: pd.Timestamp("2012-12-28 00:00:00"),
            241: pd.Timestamp("2012-12-29 00:00:00"),
            242: pd.Timestamp("2012-12-30 00:00:00"),
            243: pd.Timestamp("2012-12-31 00:00:00"),
            244: pd.Timestamp("2013-01-01 00:00:00"),
            245: pd.Timestamp("2013-01-02 00:00:00"),
            246: pd.Timestamp("2013-01-03 00:00:00"),
            247: pd.Timestamp("2013-01-04 00:00:00"),
            248: pd.Timestamp("2013-01-05 00:00:00"),
            249: pd.Timestamp("2013-01-06 00:00:00"),
            250: pd.Timestamp("2013-01-07 00:00:00"),
            251: pd.Timestamp("2013-01-08 00:00:00"),
            252: pd.Timestamp("2013-01-09 00:00:00"),
            253: pd.Timestamp("2013-01-10 00:00:00"),
            254: pd.Timestamp("2013-01-11 00:00:00"),
            255: pd.Timestamp("2013-01-12 00:00:00"),
            256: pd.Timestamp("2013-01-13 00:00:00"),
            257: pd.Timestamp("2013-01-14 00:00:00"),
            258: pd.Timestamp("2013-01-15 00:00:00"),
            259: pd.Timestamp("2013-01-16 00:00:00"),
            260: pd.Timestamp("2013-01-17 00:00:00"),
            261: pd.Timestamp("2013-01-18 00:00:00"),
            262: pd.Timestamp("2013-01-19 00:00:00"),
            263: pd.Timestamp("2013-01-20 00:00:00"),
            264: pd.Timestamp("2013-01-21 00:00:00"),
            265: pd.Timestamp("2013-01-22 00:00:00"),
            266: pd.Timestamp("2013-01-23 00:00:00"),
            267: pd.Timestamp("2013-01-24 00:00:00"),
            268: pd.Timestamp("2013-01-25 00:00:00"),
            269: pd.Timestamp("2013-01-26 00:00:00"),
            270: pd.Timestamp("2013-01-27 00:00:00"),
            271: pd.Timestamp("2013-01-28 00:00:00"),
            272: pd.Timestamp("2013-01-29 00:00:00"),
            273: pd.Timestamp("2013-01-30 00:00:00"),
            274: pd.Timestamp("2013-01-31 00:00:00"),
            275: pd.Timestamp("2013-02-01 00:00:00"),
            276: pd.Timestamp("2013-02-02 00:00:00"),
            277: pd.Timestamp("2013-02-03 00:00:00"),
            278: pd.Timestamp("2013-02-04 00:00:00"),
            279: pd.Timestamp("2013-02-05 00:00:00"),
            280: pd.Timestamp("2013-02-06 00:00:00"),
            281: pd.Timestamp("2013-02-07 00:00:00"),
            282: pd.Timestamp("2013-02-08 00:00:00"),
            283: pd.Timestamp("2013-02-09 00:00:00"),
            284: pd.Timestamp("2013-02-10 00:00:00"),
            285: pd.Timestamp("2013-02-11 00:00:00"),
            286: pd.Timestamp("2013-02-12 00:00:00"),
            287: pd.Timestamp("2013-02-13 00:00:00"),
            288: pd.Timestamp("2013-02-14 00:00:00"),
            289: pd.Timestamp("2013-02-15 00:00:00"),
            290: pd.Timestamp("2013-02-16 00:00:00"),
            291: pd.Timestamp("2013-02-17 00:00:00"),
            292: pd.Timestamp("2013-02-18 00:00:00"),
            293: pd.Timestamp("2013-02-19 00:00:00"),
            294: pd.Timestamp("2013-02-20 00:00:00"),
            295: pd.Timestamp("2013-02-21 00:00:00"),
            296: pd.Timestamp("2013-02-22 00:00:00"),
            297: pd.Timestamp("2013-02-23 00:00:00"),
            298: pd.Timestamp("2013-02-24 00:00:00"),
            299: pd.Timestamp("2013-02-25 00:00:00"),
            300: pd.Timestamp("2013-02-26 00:00:00"),
            301: pd.Timestamp("2013-02-27 00:00:00"),
            302: pd.Timestamp("2013-02-28 00:00:00"),
            303: pd.Timestamp("2013-03-01 00:00:00"),
            304: pd.Timestamp("2013-03-02 00:00:00"),
            305: pd.Timestamp("2013-03-03 00:00:00"),
            306: pd.Timestamp("2013-03-04 00:00:00"),
            307: pd.Timestamp("2013-03-05 00:00:00"),
            308: pd.Timestamp("2013-03-06 00:00:00"),
            309: pd.Timestamp("2013-03-07 00:00:00"),
            310: pd.Timestamp("2013-03-08 00:00:00"),
            311: pd.Timestamp("2013-03-09 00:00:00"),
            312: pd.Timestamp("2013-03-10 00:00:00"),
            313: pd.Timestamp("2013-03-11 00:00:00"),
            314: pd.Timestamp("2013-03-12 00:00:00"),
            315: pd.Timestamp("2013-03-13 00:00:00"),
            316: pd.Timestamp("2013-03-14 00:00:00"),
            317: pd.Timestamp("2013-03-15 00:00:00"),
            318: pd.Timestamp("2013-03-16 00:00:00"),
            319: pd.Timestamp("2013-03-17 00:00:00"),
            320: pd.Timestamp("2013-03-18 00:00:00"),
            321: pd.Timestamp("2013-03-19 00:00:00"),
            322: pd.Timestamp("2013-03-20 00:00:00"),
            323: pd.Timestamp("2013-03-21 00:00:00"),
            324: pd.Timestamp("2013-03-22 00:00:00"),
            325: pd.Timestamp("2013-03-23 00:00:00"),
            326: pd.Timestamp("2013-03-24 00:00:00"),
            327: pd.Timestamp("2013-03-25 00:00:00"),
            328: pd.Timestamp("2013-03-26 00:00:00"),
            329: pd.Timestamp("2013-03-27 00:00:00"),
            330: pd.Timestamp("2013-03-28 00:00:00"),
            331: pd.Timestamp("2013-03-29 00:00:00"),
            332: pd.Timestamp("2013-03-30 00:00:00"),
            333: pd.Timestamp("2013-03-31 00:00:00"),
            334: pd.Timestamp("2013-04-01 00:00:00"),
            335: pd.Timestamp("2013-04-02 00:00:00"),
            336: pd.Timestamp("2013-04-03 00:00:00"),
            337: pd.Timestamp("2013-04-04 00:00:00"),
            338: pd.Timestamp("2013-04-05 00:00:00"),
            339: pd.Timestamp("2013-04-06 00:00:00"),
            340: pd.Timestamp("2013-04-07 00:00:00"),
            341: pd.Timestamp("2013-04-08 00:00:00"),
            342: pd.Timestamp("2013-04-09 00:00:00"),
            343: pd.Timestamp("2013-04-10 00:00:00"),
            344: pd.Timestamp("2013-04-11 00:00:00"),
            345: pd.Timestamp("2013-04-12 00:00:00"),
            346: pd.Timestamp("2013-04-13 00:00:00"),
            347: pd.Timestamp("2013-04-14 00:00:00"),
            348: pd.Timestamp("2013-04-15 00:00:00"),
            349: pd.Timestamp("2013-04-16 00:00:00"),
            350: pd.Timestamp("2013-04-17 00:00:00"),
            351: pd.Timestamp("2013-04-18 00:00:00"),
            352: pd.Timestamp("2013-04-19 00:00:00"),
            353: pd.Timestamp("2013-04-20 00:00:00"),
            354: pd.Timestamp("2013-04-21 00:00:00"),
            355: pd.Timestamp("2013-04-22 00:00:00"),
            356: pd.Timestamp("2013-04-23 00:00:00"),
            357: pd.Timestamp("2013-04-24 00:00:00"),
            358: pd.Timestamp("2013-04-25 00:00:00"),
            359: pd.Timestamp("2013-04-26 00:00:00"),
            360: pd.Timestamp("2013-04-27 00:00:00"),
            361: pd.Timestamp("2013-04-28 00:00:00"),
            362: pd.Timestamp("2013-04-29 00:00:00"),
            363: pd.Timestamp("2013-04-30 00:00:00"),
            364: pd.Timestamp("2013-05-01 00:00:00"),
            365: pd.Timestamp("2013-05-02 00:00:00"),
            366: pd.Timestamp("2013-05-03 00:00:00"),
            367: pd.Timestamp("2013-05-04 00:00:00"),
            368: pd.Timestamp("2013-05-05 00:00:00"),
            369: pd.Timestamp("2013-05-06 00:00:00"),
            370: pd.Timestamp("2013-05-07 00:00:00"),
            371: pd.Timestamp("2013-05-08 00:00:00"),
            372: pd.Timestamp("2013-05-09 00:00:00"),
            373: pd.Timestamp("2013-05-10 00:00:00"),
            374: pd.Timestamp("2013-05-11 00:00:00"),
            375: pd.Timestamp("2013-05-12 00:00:00"),
            376: pd.Timestamp("2013-05-13 00:00:00"),
            377: pd.Timestamp("2013-05-14 00:00:00"),
            378: pd.Timestamp("2013-05-15 00:00:00"),
            379: pd.Timestamp("2013-05-16 00:00:00"),
            380: pd.Timestamp("2013-05-17 00:00:00"),
            381: pd.Timestamp("2013-05-18 00:00:00"),
            382: pd.Timestamp("2013-05-19 00:00:00"),
            383: pd.Timestamp("2013-05-20 00:00:00"),
            384: pd.Timestamp("2013-05-21 00:00:00"),
            385: pd.Timestamp("2013-05-22 00:00:00"),
            386: pd.Timestamp("2013-05-23 00:00:00"),
            387: pd.Timestamp("2013-05-24 00:00:00"),
            388: pd.Timestamp("2013-05-25 00:00:00"),
            389: pd.Timestamp("2013-05-26 00:00:00"),
            390: pd.Timestamp("2013-05-27 00:00:00"),
            391: pd.Timestamp("2013-05-28 00:00:00"),
            392: pd.Timestamp("2013-05-29 00:00:00"),
            393: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 8.348604308646497,
            1: 8.348964254851197,
            2: 8.349324201055898,
            3: 8.349684147260598,
            4: 8.350044093465298,
            5: 8.350404039669998,
            6: 8.3507639858747,
            7: 8.3511239320794,
            8: 8.3514838782841,
            9: 8.351843824488801,
            10: 8.352203770693501,
            11: 8.352563716898201,
            12: 8.352923663102903,
            13: 8.353283609307603,
            14: 8.353643555512303,
            15: 8.354003501717003,
            16: 8.354363447921704,
            17: 8.354723394126404,
            18: 8.355083340331104,
            19: 8.355443286535806,
            20: 8.355803232740506,
            21: 8.356163178945206,
            22: 8.356523125149906,
            23: 8.356883071354607,
            24: 8.357243017559307,
            25: 8.357602963764007,
            26: 8.357962909968709,
            27: 8.358322856173409,
            28: 8.358682802378109,
            29: 8.35904274858281,
            30: 8.35940269478751,
            31: 8.35976264099221,
            32: 8.36012258719691,
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            300: -np.inf,
            301: -np.inf,
            302: -np.inf,
            303: -np.inf,
            304: -np.inf,
            305: -np.inf,
            306: -np.inf,
            307: -np.inf,
            308: -np.inf,
            309: -np.inf,
            310: -np.inf,
            311: -np.inf,
            312: -np.inf,
            313: -np.inf,
            314: -np.inf,
            315: -np.inf,
            316: -np.inf,
            317: -np.inf,
            318: -np.inf,
            319: -np.inf,
            320: -np.inf,
            321: -np.inf,
            322: -np.inf,
            323: -np.inf,
            324: -np.inf,
            325: -np.inf,
            326: -np.inf,
            327: -np.inf,
            328: -np.inf,
            329: -np.inf,
            330: -np.inf,
            331: -np.inf,
            332: -np.inf,
            333: -np.inf,
            334: -np.inf,
            335: -np.inf,
            336: -np.inf,
            337: -np.inf,
            338: -np.inf,
            339: -np.inf,
            340: -np.inf,
            341: -np.inf,
            342: -np.inf,
            343: -np.inf,
            344: -np.inf,
            345: -np.inf,
            346: -np.inf,
            347: -np.inf,
            348: -np.inf,
            349: -np.inf,
            350: -np.inf,
            351: -np.inf,
            352: -np.inf,
            353: -np.inf,
            354: -np.inf,
            355: -np.inf,
            356: -np.inf,
            357: -np.inf,
            358: -np.inf,
            359: -np.inf,
            360: -np.inf,
            361: -np.inf,
            362: -np.inf,
            363: -np.inf,
            364: -np.inf,
            365: -np.inf,
            366: -np.inf,
            367: -np.inf,
            368: -np.inf,
            369: -np.inf,
            370: -np.inf,
            371: -np.inf,
            372: -np.inf,
            373: -np.inf,
            374: -np.inf,
            375: -np.inf,
            376: -np.inf,
            377: -np.inf,
            378: -np.inf,
            379: -np.inf,
            380: -np.inf,
            381: -np.inf,
            382: -np.inf,
            383: -np.inf,
            384: -np.inf,
            385: -np.inf,
            386: -np.inf,
            387: -np.inf,
            388: -np.inf,
            389: -np.inf,
            390: -np.inf,
            391: -np.inf,
            392: -np.inf,
            393: -np.inf,
        },
        "fcst_upper": {
            0: np.inf,
            1: np.inf,
            2: np.inf,
            3: np.inf,
            4: np.inf,
            5: np.inf,
            6: np.inf,
            7: np.inf,
            8: np.inf,
            9: np.inf,
            10: np.inf,
            11: np.inf,
            12: np.inf,
            13: np.inf,
            14: np.inf,
            15: np.inf,
            16: np.inf,
            17: np.inf,
            18: np.inf,
            19: np.inf,
            20: np.inf,
            21: np.inf,
            22: np.inf,
            23: np.inf,
            24: np.inf,
            25: np.inf,
            26: np.inf,
            27: np.inf,
            28: np.inf,
            29: np.inf,
            30: np.inf,
            31: np.inf,
            32: np.inf,
            33: np.inf,
            34: np.inf,
            35: np.inf,
            36: np.inf,
            37: np.inf,
            38: np.inf,
            39: np.inf,
            40: np.inf,
            41: np.inf,
            42: np.inf,
            43: np.inf,
            44: np.inf,
            45: np.inf,
            46: np.inf,
            47: np.inf,
            48: np.inf,
            49: np.inf,
            50: np.inf,
            51: np.inf,
            52: np.inf,
            53: np.inf,
            54: np.inf,
            55: np.inf,
            56: np.inf,
            57: np.inf,
            58: np.inf,
            59: np.inf,
            60: np.inf,
            61: np.inf,
            62: np.inf,
            63: np.inf,
            64: np.inf,
            65: np.inf,
            66: np.inf,
            67: np.inf,
            68: np.inf,
            69: np.inf,
            70: np.inf,
            71: np.inf,
            72: np.inf,
            73: np.inf,
            74: np.inf,
            75: np.inf,
            76: np.inf,
            77: np.inf,
            78: np.inf,
            79: np.inf,
            80: np.inf,
            81: np.inf,
            82: np.inf,
            83: np.inf,
            84: np.inf,
            85: np.inf,
            86: np.inf,
            87: np.inf,
            88: np.inf,
            89: np.inf,
            90: np.inf,
            91: np.inf,
            92: np.inf,
            93: np.inf,
            94: np.inf,
            95: np.inf,
            96: np.inf,
            97: np.inf,
            98: np.inf,
            99: np.inf,
            100: np.inf,
            101: np.inf,
            102: np.inf,
            103: np.inf,
            104: np.inf,
            105: np.inf,
            106: np.inf,
            107: np.inf,
            108: np.inf,
            109: np.inf,
            110: np.inf,
            111: np.inf,
            112: np.inf,
            113: np.inf,
            114: np.inf,
            115: np.inf,
            116: np.inf,
            117: np.inf,
            118: np.inf,
            119: np.inf,
            120: np.inf,
            121: np.inf,
            122: np.inf,
            123: np.inf,
            124: np.inf,
            125: np.inf,
            126: np.inf,
            127: np.inf,
            128: np.inf,
            129: np.inf,
            130: np.inf,
            131: np.inf,
            132: np.inf,
            133: np.inf,
            134: np.inf,
            135: np.inf,
            136: np.inf,
            137: np.inf,
            138: np.inf,
            139: np.inf,
            140: np.inf,
            141: np.inf,
            142: np.inf,
            143: np.inf,
            144: np.inf,
            145: np.inf,
            146: np.inf,
            147: np.inf,
            148: np.inf,
            149: np.inf,
            150: np.inf,
            151: np.inf,
            152: np.inf,
            153: np.inf,
            154: np.inf,
            155: np.inf,
            156: np.inf,
            157: np.inf,
            158: np.inf,
            159: np.inf,
            160: np.inf,
            161: np.inf,
            162: np.inf,
            163: np.inf,
            164: np.inf,
            165: np.inf,
            166: np.inf,
            167: np.inf,
            168: np.inf,
            169: np.inf,
            170: np.inf,
            171: np.inf,
            172: np.inf,
            173: np.inf,
            174: np.inf,
            175: np.inf,
            176: np.inf,
            177: np.inf,
            178: np.inf,
            179: np.inf,
            180: np.inf,
            181: np.inf,
            182: np.inf,
            183: np.inf,
            184: np.inf,
            185: np.inf,
            186: np.inf,
            187: np.inf,
            188: np.inf,
            189: np.inf,
            190: np.inf,
            191: np.inf,
            192: np.inf,
            193: np.inf,
            194: np.inf,
            195: np.inf,
            196: np.inf,
            197: np.inf,
            198: np.inf,
            199: np.inf,
            200: np.inf,
            201: np.inf,
            202: np.inf,
            203: np.inf,
            204: np.inf,
            205: np.inf,
            206: np.inf,
            207: np.inf,
            208: np.inf,
            209: np.inf,
            210: np.inf,
            211: np.inf,
            212: np.inf,
            213: np.inf,
            214: np.inf,
            215: np.inf,
            216: np.inf,
            217: np.inf,
            218: np.inf,
            219: np.inf,
            220: np.inf,
            221: np.inf,
            222: np.inf,
            223: np.inf,
            224: np.inf,
            225: np.inf,
            226: np.inf,
            227: np.inf,
            228: np.inf,
            229: np.inf,
            230: np.inf,
            231: np.inf,
            232: np.inf,
            233: np.inf,
            234: np.inf,
            235: np.inf,
            236: np.inf,
            237: np.inf,
            238: np.inf,
            239: np.inf,
            240: np.inf,
            241: np.inf,
            242: np.inf,
            243: np.inf,
            244: np.inf,
            245: np.inf,
            246: np.inf,
            247: np.inf,
            248: np.inf,
            249: np.inf,
            250: np.inf,
            251: np.inf,
            252: np.inf,
            253: np.inf,
            254: np.inf,
            255: np.inf,
            256: np.inf,
            257: np.inf,
            258: np.inf,
            259: np.inf,
            260: np.inf,
            261: np.inf,
            262: np.inf,
            263: np.inf,
            264: np.inf,
            265: np.inf,
            266: np.inf,
            267: np.inf,
            268: np.inf,
            269: np.inf,
            270: np.inf,
            271: np.inf,
            272: np.inf,
            273: np.inf,
            274: np.inf,
            275: np.inf,
            276: np.inf,
            277: np.inf,
            278: np.inf,
            279: np.inf,
            280: np.inf,
            281: np.inf,
            282: np.inf,
            283: np.inf,
            284: np.inf,
            285: np.inf,
            286: np.inf,
            287: np.inf,
            288: np.inf,
            289: np.inf,
            290: np.inf,
            291: np.inf,
            292: np.inf,
            293: np.inf,
            294: np.inf,
            295: np.inf,
            296: np.inf,
            297: np.inf,
            298: np.inf,
            299: np.inf,
            300: np.inf,
            301: np.inf,
            302: np.inf,
            303: np.inf,
            304: np.inf,
            305: np.inf,
            306: np.inf,
            307: np.inf,
            308: np.inf,
            309: np.inf,
            310: np.inf,
            311: np.inf,
            312: np.inf,
            313: np.inf,
            314: np.inf,
            315: np.inf,
            316: np.inf,
            317: np.inf,
            318: np.inf,
            319: np.inf,
            320: np.inf,
            321: np.inf,
            322: np.inf,
            323: np.inf,
            324: np.inf,
            325: np.inf,
            326: np.inf,
            327: np.inf,
            328: np.inf,
            329: np.inf,
            330: np.inf,
            331: np.inf,
            332: np.inf,
            333: np.inf,
            334: np.inf,
            335: np.inf,
            336: np.inf,
            337: np.inf,
            338: np.inf,
            339: np.inf,
            340: np.inf,
            341: np.inf,
            342: np.inf,
            343: np.inf,
            344: np.inf,
            345: np.inf,
            346: np.inf,
            347: np.inf,
            348: np.inf,
            349: np.inf,
            350: np.inf,
            351: np.inf,
            352: np.inf,
            353: np.inf,
            354: np.inf,
            355: np.inf,
            356: np.inf,
            357: np.inf,
            358: np.inf,
            359: np.inf,
            360: np.inf,
            361: np.inf,
            362: np.inf,
            363: np.inf,
            364: np.inf,
            365: np.inf,
            366: np.inf,
            367: np.inf,
            368: np.inf,
            369: np.inf,
            370: np.inf,
            371: np.inf,
            372: np.inf,
            373: np.inf,
            374: np.inf,
            375: np.inf,
            376: np.inf,
            377: np.inf,
            378: np.inf,
            379: np.inf,
            380: np.inf,
            381: np.inf,
            382: np.inf,
            383: np.inf,
            384: np.inf,
            385: np.inf,
            386: np.inf,
            387: np.inf,
            388: np.inf,
            389: np.inf,
            390: np.inf,
            391: np.inf,
            392: np.inf,
            393: np.inf,
        },
    }
)

PEYTON_FCST_LINEAR_INVALID_NEG_ONE = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2012-05-02 00:00:00"),
            1: pd.Timestamp("2012-05-03 00:00:00"),
            2: pd.Timestamp("2012-05-04 00:00:00"),
            3: pd.Timestamp("2012-05-05 00:00:00"),
            4: pd.Timestamp("2012-05-06 00:00:00"),
            5: pd.Timestamp("2012-05-07 00:00:00"),
            6: pd.Timestamp("2012-05-08 00:00:00"),
            7: pd.Timestamp("2012-05-09 00:00:00"),
            8: pd.Timestamp("2012-05-10 00:00:00"),
            9: pd.Timestamp("2012-05-11 00:00:00"),
            10: pd.Timestamp("2012-05-12 00:00:00"),
            11: pd.Timestamp("2012-05-13 00:00:00"),
            12: pd.Timestamp("2012-05-14 00:00:00"),
            13: pd.Timestamp("2012-05-15 00:00:00"),
            14: pd.Timestamp("2012-05-16 00:00:00"),
            15: pd.Timestamp("2012-05-17 00:00:00"),
            16: pd.Timestamp("2012-05-18 00:00:00"),
            17: pd.Timestamp("2012-05-19 00:00:00"),
            18: pd.Timestamp("2012-05-20 00:00:00"),
            19: pd.Timestamp("2012-05-21 00:00:00"),
            20: pd.Timestamp("2012-05-22 00:00:00"),
            21: pd.Timestamp("2012-05-23 00:00:00"),
            22: pd.Timestamp("2012-05-24 00:00:00"),
            23: pd.Timestamp("2012-05-25 00:00:00"),
            24: pd.Timestamp("2012-05-26 00:00:00"),
            25: pd.Timestamp("2012-05-27 00:00:00"),
            26: pd.Timestamp("2012-05-28 00:00:00"),
            27: pd.Timestamp("2012-05-29 00:00:00"),
            28: pd.Timestamp("2012-05-30 00:00:00"),
            29: pd.Timestamp("2012-05-31 00:00:00"),
            30: pd.Timestamp("2012-06-01 00:00:00"),
            31: pd.Timestamp("2012-06-02 00:00:00"),
            32: pd.Timestamp("2012-06-03 00:00:00"),
            33: pd.Timestamp("2012-06-04 00:00:00"),
            34: pd.Timestamp("2012-06-05 00:00:00"),
            35: pd.Timestamp("2012-06-06 00:00:00"),
            36: pd.Timestamp("2012-06-07 00:00:00"),
            37: pd.Timestamp("2012-06-08 00:00:00"),
            38: pd.Timestamp("2012-06-09 00:00:00"),
            39: pd.Timestamp("2012-06-10 00:00:00"),
            40: pd.Timestamp("2012-06-11 00:00:00"),
            41: pd.Timestamp("2012-06-12 00:00:00"),
            42: pd.Timestamp("2012-06-13 00:00:00"),
            43: pd.Timestamp("2012-06-14 00:00:00"),
            44: pd.Timestamp("2012-06-15 00:00:00"),
            45: pd.Timestamp("2012-06-16 00:00:00"),
            46: pd.Timestamp("2012-06-17 00:00:00"),
            47: pd.Timestamp("2012-06-18 00:00:00"),
            48: pd.Timestamp("2012-06-19 00:00:00"),
            49: pd.Timestamp("2012-06-20 00:00:00"),
            50: pd.Timestamp("2012-06-21 00:00:00"),
            51: pd.Timestamp("2012-06-22 00:00:00"),
            52: pd.Timestamp("2012-06-23 00:00:00"),
            53: pd.Timestamp("2012-06-24 00:00:00"),
            54: pd.Timestamp("2012-06-25 00:00:00"),
            55: pd.Timestamp("2012-06-26 00:00:00"),
            56: pd.Timestamp("2012-06-27 00:00:00"),
            57: pd.Timestamp("2012-06-28 00:00:00"),
            58: pd.Timestamp("2012-06-29 00:00:00"),
            59: pd.Timestamp("2012-06-30 00:00:00"),
            60: pd.Timestamp("2012-07-01 00:00:00"),
            61: pd.Timestamp("2012-07-02 00:00:00"),
            62: pd.Timestamp("2012-07-03 00:00:00"),
            63: pd.Timestamp("2012-07-04 00:00:00"),
            64: pd.Timestamp("2012-07-05 00:00:00"),
            65: pd.Timestamp("2012-07-06 00:00:00"),
            66: pd.Timestamp("2012-07-07 00:00:00"),
            67: pd.Timestamp("2012-07-08 00:00:00"),
            68: pd.Timestamp("2012-07-09 00:00:00"),
            69: pd.Timestamp("2012-07-10 00:00:00"),
            70: pd.Timestamp("2012-07-11 00:00:00"),
            71: pd.Timestamp("2012-07-12 00:00:00"),
            72: pd.Timestamp("2012-07-13 00:00:00"),
            73: pd.Timestamp("2012-07-14 00:00:00"),
            74: pd.Timestamp("2012-07-15 00:00:00"),
            75: pd.Timestamp("2012-07-16 00:00:00"),
            76: pd.Timestamp("2012-07-17 00:00:00"),
            77: pd.Timestamp("2012-07-18 00:00:00"),
            78: pd.Timestamp("2012-07-19 00:00:00"),
            79: pd.Timestamp("2012-07-20 00:00:00"),
            80: pd.Timestamp("2012-07-21 00:00:00"),
            81: pd.Timestamp("2012-07-22 00:00:00"),
            82: pd.Timestamp("2012-07-23 00:00:00"),
            83: pd.Timestamp("2012-07-24 00:00:00"),
            84: pd.Timestamp("2012-07-25 00:00:00"),
            85: pd.Timestamp("2012-07-26 00:00:00"),
            86: pd.Timestamp("2012-07-27 00:00:00"),
            87: pd.Timestamp("2012-07-28 00:00:00"),
            88: pd.Timestamp("2012-07-29 00:00:00"),
            89: pd.Timestamp("2012-07-30 00:00:00"),
            90: pd.Timestamp("2012-07-31 00:00:00"),
            91: pd.Timestamp("2012-08-01 00:00:00"),
            92: pd.Timestamp("2012-08-02 00:00:00"),
            93: pd.Timestamp("2012-08-03 00:00:00"),
            94: pd.Timestamp("2012-08-04 00:00:00"),
            95: pd.Timestamp("2012-08-05 00:00:00"),
            96: pd.Timestamp("2012-08-06 00:00:00"),
            97: pd.Timestamp("2012-08-07 00:00:00"),
            98: pd.Timestamp("2012-08-08 00:00:00"),
            99: pd.Timestamp("2012-08-09 00:00:00"),
            100: pd.Timestamp("2012-08-10 00:00:00"),
            101: pd.Timestamp("2012-08-11 00:00:00"),
            102: pd.Timestamp("2012-08-12 00:00:00"),
            103: pd.Timestamp("2012-08-13 00:00:00"),
            104: pd.Timestamp("2012-08-14 00:00:00"),
            105: pd.Timestamp("2012-08-15 00:00:00"),
            106: pd.Timestamp("2012-08-16 00:00:00"),
            107: pd.Timestamp("2012-08-17 00:00:00"),
            108: pd.Timestamp("2012-08-18 00:00:00"),
            109: pd.Timestamp("2012-08-19 00:00:00"),
            110: pd.Timestamp("2012-08-20 00:00:00"),
            111: pd.Timestamp("2012-08-21 00:00:00"),
            112: pd.Timestamp("2012-08-22 00:00:00"),
            113: pd.Timestamp("2012-08-23 00:00:00"),
            114: pd.Timestamp("2012-08-24 00:00:00"),
            115: pd.Timestamp("2012-08-25 00:00:00"),
            116: pd.Timestamp("2012-08-26 00:00:00"),
            117: pd.Timestamp("2012-08-27 00:00:00"),
            118: pd.Timestamp("2012-08-28 00:00:00"),
            119: pd.Timestamp("2012-08-29 00:00:00"),
            120: pd.Timestamp("2012-08-30 00:00:00"),
            121: pd.Timestamp("2012-08-31 00:00:00"),
            122: pd.Timestamp("2012-09-01 00:00:00"),
            123: pd.Timestamp("2012-09-02 00:00:00"),
            124: pd.Timestamp("2012-09-03 00:00:00"),
            125: pd.Timestamp("2012-09-04 00:00:00"),
            126: pd.Timestamp("2012-09-05 00:00:00"),
            127: pd.Timestamp("2012-09-06 00:00:00"),
            128: pd.Timestamp("2012-09-07 00:00:00"),
            129: pd.Timestamp("2012-09-08 00:00:00"),
            130: pd.Timestamp("2012-09-09 00:00:00"),
            131: pd.Timestamp("2012-09-10 00:00:00"),
            132: pd.Timestamp("2012-09-11 00:00:00"),
            133: pd.Timestamp("2012-09-12 00:00:00"),
            134: pd.Timestamp("2012-09-13 00:00:00"),
            135: pd.Timestamp("2012-09-14 00:00:00"),
            136: pd.Timestamp("2012-09-15 00:00:00"),
            137: pd.Timestamp("2012-09-16 00:00:00"),
            138: pd.Timestamp("2012-09-17 00:00:00"),
            139: pd.Timestamp("2012-09-18 00:00:00"),
            140: pd.Timestamp("2012-09-19 00:00:00"),
            141: pd.Timestamp("2012-09-20 00:00:00"),
            142: pd.Timestamp("2012-09-21 00:00:00"),
            143: pd.Timestamp("2012-09-22 00:00:00"),
            144: pd.Timestamp("2012-09-23 00:00:00"),
            145: pd.Timestamp("2012-09-24 00:00:00"),
            146: pd.Timestamp("2012-09-25 00:00:00"),
            147: pd.Timestamp("2012-09-26 00:00:00"),
            148: pd.Timestamp("2012-09-27 00:00:00"),
            149: pd.Timestamp("2012-09-28 00:00:00"),
            150: pd.Timestamp("2012-09-29 00:00:00"),
            151: pd.Timestamp("2012-09-30 00:00:00"),
            152: pd.Timestamp("2012-10-01 00:00:00"),
            153: pd.Timestamp("2012-10-02 00:00:00"),
            154: pd.Timestamp("2012-10-03 00:00:00"),
            155: pd.Timestamp("2012-10-04 00:00:00"),
            156: pd.Timestamp("2012-10-05 00:00:00"),
            157: pd.Timestamp("2012-10-06 00:00:00"),
            158: pd.Timestamp("2012-10-07 00:00:00"),
            159: pd.Timestamp("2012-10-08 00:00:00"),
            160: pd.Timestamp("2012-10-09 00:00:00"),
            161: pd.Timestamp("2012-10-10 00:00:00"),
            162: pd.Timestamp("2012-10-11 00:00:00"),
            163: pd.Timestamp("2012-10-12 00:00:00"),
            164: pd.Timestamp("2012-10-13 00:00:00"),
            165: pd.Timestamp("2012-10-14 00:00:00"),
            166: pd.Timestamp("2012-10-15 00:00:00"),
            167: pd.Timestamp("2012-10-16 00:00:00"),
            168: pd.Timestamp("2012-10-17 00:00:00"),
            169: pd.Timestamp("2012-10-18 00:00:00"),
            170: pd.Timestamp("2012-10-19 00:00:00"),
            171: pd.Timestamp("2012-10-20 00:00:00"),
            172: pd.Timestamp("2012-10-21 00:00:00"),
            173: pd.Timestamp("2012-10-22 00:00:00"),
            174: pd.Timestamp("2012-10-23 00:00:00"),
            175: pd.Timestamp("2012-10-24 00:00:00"),
            176: pd.Timestamp("2012-10-25 00:00:00"),
            177: pd.Timestamp("2012-10-26 00:00:00"),
            178: pd.Timestamp("2012-10-27 00:00:00"),
            179: pd.Timestamp("2012-10-28 00:00:00"),
            180: pd.Timestamp("2012-10-29 00:00:00"),
            181: pd.Timestamp("2012-10-30 00:00:00"),
            182: pd.Timestamp("2012-10-31 00:00:00"),
            183: pd.Timestamp("2012-11-01 00:00:00"),
            184: pd.Timestamp("2012-11-02 00:00:00"),
            185: pd.Timestamp("2012-11-03 00:00:00"),
            186: pd.Timestamp("2012-11-04 00:00:00"),
            187: pd.Timestamp("2012-11-05 00:00:00"),
            188: pd.Timestamp("2012-11-06 00:00:00"),
            189: pd.Timestamp("2012-11-07 00:00:00"),
            190: pd.Timestamp("2012-11-08 00:00:00"),
            191: pd.Timestamp("2012-11-09 00:00:00"),
            192: pd.Timestamp("2012-11-10 00:00:00"),
            193: pd.Timestamp("2012-11-11 00:00:00"),
            194: pd.Timestamp("2012-11-12 00:00:00"),
            195: pd.Timestamp("2012-11-13 00:00:00"),
            196: pd.Timestamp("2012-11-14 00:00:00"),
            197: pd.Timestamp("2012-11-15 00:00:00"),
            198: pd.Timestamp("2012-11-16 00:00:00"),
            199: pd.Timestamp("2012-11-17 00:00:00"),
            200: pd.Timestamp("2012-11-18 00:00:00"),
            201: pd.Timestamp("2012-11-19 00:00:00"),
            202: pd.Timestamp("2012-11-20 00:00:00"),
            203: pd.Timestamp("2012-11-21 00:00:00"),
            204: pd.Timestamp("2012-11-22 00:00:00"),
            205: pd.Timestamp("2012-11-23 00:00:00"),
            206: pd.Timestamp("2012-11-24 00:00:00"),
            207: pd.Timestamp("2012-11-25 00:00:00"),
            208: pd.Timestamp("2012-11-26 00:00:00"),
            209: pd.Timestamp("2012-11-27 00:00:00"),
            210: pd.Timestamp("2012-11-28 00:00:00"),
            211: pd.Timestamp("2012-11-29 00:00:00"),
            212: pd.Timestamp("2012-11-30 00:00:00"),
            213: pd.Timestamp("2012-12-01 00:00:00"),
            214: pd.Timestamp("2012-12-02 00:00:00"),
            215: pd.Timestamp("2012-12-03 00:00:00"),
            216: pd.Timestamp("2012-12-04 00:00:00"),
            217: pd.Timestamp("2012-12-05 00:00:00"),
            218: pd.Timestamp("2012-12-06 00:00:00"),
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            225: pd.Timestamp("2012-12-13 00:00:00"),
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            229: pd.Timestamp("2012-12-17 00:00:00"),
            230: pd.Timestamp("2012-12-18 00:00:00"),
            231: pd.Timestamp("2012-12-19 00:00:00"),
            232: pd.Timestamp("2012-12-20 00:00:00"),
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            235: pd.Timestamp("2012-12-23 00:00:00"),
            236: pd.Timestamp("2012-12-24 00:00:00"),
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            239: pd.Timestamp("2012-12-27 00:00:00"),
            240: pd.Timestamp("2012-12-28 00:00:00"),
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            243: pd.Timestamp("2012-12-31 00:00:00"),
            244: pd.Timestamp("2013-01-01 00:00:00"),
            245: pd.Timestamp("2013-01-02 00:00:00"),
            246: pd.Timestamp("2013-01-03 00:00:00"),
            247: pd.Timestamp("2013-01-04 00:00:00"),
            248: pd.Timestamp("2013-01-05 00:00:00"),
            249: pd.Timestamp("2013-01-06 00:00:00"),
            250: pd.Timestamp("2013-01-07 00:00:00"),
            251: pd.Timestamp("2013-01-08 00:00:00"),
            252: pd.Timestamp("2013-01-09 00:00:00"),
            253: pd.Timestamp("2013-01-10 00:00:00"),
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            313: np.nan,
            314: np.nan,
            315: np.nan,
            316: np.nan,
            317: np.nan,
            318: np.nan,
            319: np.nan,
            320: np.nan,
            321: np.nan,
            322: np.nan,
            323: np.nan,
            324: np.nan,
            325: np.nan,
            326: np.nan,
            327: np.nan,
            328: np.nan,
            329: np.nan,
            330: np.nan,
            331: np.nan,
            332: np.nan,
            333: np.nan,
            334: np.nan,
            335: np.nan,
            336: np.nan,
            337: np.nan,
            338: np.nan,
            339: np.nan,
            340: np.nan,
            341: np.nan,
            342: np.nan,
            343: np.nan,
            344: np.nan,
            345: np.nan,
            346: np.nan,
            347: np.nan,
            348: np.nan,
            349: np.nan,
            350: np.nan,
            351: np.nan,
            352: np.nan,
            353: np.nan,
            354: np.nan,
            355: np.nan,
            356: np.nan,
            357: np.nan,
            358: np.nan,
            359: np.nan,
            360: np.nan,
            361: np.nan,
            362: np.nan,
            363: np.nan,
            364: np.nan,
            365: np.nan,
            366: np.nan,
            367: np.nan,
            368: np.nan,
            369: np.nan,
            370: np.nan,
            371: np.nan,
            372: np.nan,
            373: np.nan,
            374: np.nan,
            375: np.nan,
            376: np.nan,
            377: np.nan,
            378: np.nan,
            379: np.nan,
            380: np.nan,
            381: np.nan,
            382: np.nan,
            383: np.nan,
            384: np.nan,
            385: np.nan,
            386: np.nan,
            387: np.nan,
            388: np.nan,
            389: np.nan,
            390: np.nan,
            391: np.nan,
            392: np.nan,
            393: np.nan,
        },
    }
)

PEYTON_FCST_LINEAR_NAN = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: np.nan,
            1: np.nan,
            2: np.nan,
            3: np.nan,
            4: np.nan,
            5: np.nan,
            6: np.nan,
            7: np.nan,
            8: np.nan,
            9: np.nan,
            10: np.nan,
            11: np.nan,
            12: np.nan,
            13: np.nan,
            14: np.nan,
            15: np.nan,
            16: np.nan,
            17: np.nan,
            18: np.nan,
            19: np.nan,
            20: np.nan,
            21: np.nan,
            22: np.nan,
            23: np.nan,
            24: np.nan,
            25: np.nan,
            26: np.nan,
            27: np.nan,
            28: np.nan,
            29: np.nan,
        },
        "fcst_lower": {
            0: np.nan,
            1: np.nan,
            2: np.nan,
            3: np.nan,
            4: np.nan,
            5: np.nan,
            6: np.nan,
            7: np.nan,
            8: np.nan,
            9: np.nan,
            10: np.nan,
            11: np.nan,
            12: np.nan,
            13: np.nan,
            14: np.nan,
            15: np.nan,
            16: np.nan,
            17: np.nan,
            18: np.nan,
            19: np.nan,
            20: np.nan,
            21: np.nan,
            22: np.nan,
            23: np.nan,
            24: np.nan,
            25: np.nan,
            26: np.nan,
            27: np.nan,
            28: np.nan,
            29: np.nan,
        },
        "fcst_upper": {
            0: np.nan,
            1: np.nan,
            2: np.nan,
            3: np.nan,
            4: np.nan,
            5: np.nan,
            6: np.nan,
            7: np.nan,
            8: np.nan,
            9: np.nan,
            10: np.nan,
            11: np.nan,
            12: np.nan,
            13: np.nan,
            14: np.nan,
            15: np.nan,
            16: np.nan,
            17: np.nan,
            18: np.nan,
            19: np.nan,
            20: np.nan,
            21: np.nan,
            22: np.nan,
            23: np.nan,
            24: np.nan,
            25: np.nan,
            26: np.nan,
            27: np.nan,
            28: np.nan,
            29: np.nan,
        },
    }
)

PEYTON_INPUT_NAN = pd.DataFrame(
    {
        "time": {
            0: "2012-05-02",
            1: "2012-05-03",
            2: "2012-05-04",
            3: "2012-05-05",
            4: "2012-05-06",
            5: "2012-05-07",
            6: "2012-05-08",
            7: "2012-05-09",
            8: "2012-05-10",
            9: "2012-05-11",
            10: "2012-05-12",
            11: "2012-05-13",
            12: "2012-05-14",
            13: "2012-05-15",
            14: "2012-05-16",
            15: "2012-05-17",
            16: "2012-05-18",
            17: "2012-05-19",
            18: "2012-05-20",
            19: "2012-05-21",
            20: "2012-05-22",
            21: "2012-05-23",
            22: "2012-05-24",
            23: "2012-05-25",
            24: "2012-05-26",
            25: "2012-05-27",
            26: "2012-05-28",
            27: "2012-05-29",
            28: "2012-05-30",
            29: "2012-05-31",
            30: "2012-06-01",
            31: "2012-06-02",
            32: "2012-06-03",
            33: "2012-06-04",
            34: "2012-06-05",
            35: "2012-06-06",
            36: "2012-06-07",
            37: "2012-06-08",
            38: "2012-06-09",
            39: "2012-06-10",
            40: "2012-06-11",
            41: "2012-06-12",
            42: "2012-06-13",
            43: "2012-06-14",
            44: "2012-06-15",
            45: "2012-06-16",
            46: "2012-06-17",
            47: "2012-06-18",
            48: "2012-06-19",
            49: "2012-06-20",
            50: "2012-06-21",
            51: "2012-06-22",
            52: "2012-06-23",
            53: "2012-06-24",
            54: "2012-06-25",
            55: "2012-06-26",
            56: "2012-06-27",
            57: "2012-06-28",
            58: "2012-06-29",
            59: "2012-06-30",
            60: "2012-07-01",
            61: "2012-07-02",
            62: "2012-07-03",
            63: "2012-07-04",
            64: "2012-07-05",
            65: "2012-07-06",
            66: "2012-07-07",
            67: "2012-07-08",
            68: "2012-07-09",
            69: "2012-07-10",
            70: "2012-07-11",
            71: "2012-07-12",
            72: "2012-07-13",
            73: "2012-07-14",
            74: "2012-07-15",
            75: "2012-07-16",
            76: "2012-07-17",
            77: "2012-07-18",
            78: "2012-07-19",
            79: "2012-07-20",
            80: "2012-07-21",
            81: "2012-07-22",
            82: "2012-07-23",
            83: "2012-07-24",
            84: "2012-07-25",
            85: "2012-07-26",
            86: "2012-07-27",
            87: "2012-07-28",
            88: "2012-07-29",
            89: "2012-07-30",
            90: "2012-07-31",
            91: "2012-08-01",
            92: "2012-08-02",
            93: "2012-08-03",
            94: "2012-08-04",
            95: "2012-08-05",
            96: "2012-08-06",
            97: "2012-08-07",
            98: "2012-08-08",
            99: "2012-08-09",
            100: "2012-08-10",
            101: "2012-08-11",
            102: "2012-08-12",
            103: "2012-08-13",
            104: "2012-08-14",
            105: "2012-08-15",
            106: "2012-08-16",
            107: "2012-08-17",
            108: "2012-08-18",
            109: "2012-08-19",
            110: "2012-08-20",
            111: "2012-08-21",
            112: "2012-08-22",
            113: "2012-08-23",
            114: "2012-08-24",
            115: "2012-08-25",
            116: "2012-08-26",
            117: "2012-08-27",
            118: "2012-08-28",
            119: "2012-08-29",
            120: "2012-08-30",
            121: "2012-08-31",
            122: "2012-09-01",
            123: "2012-09-02",
            124: "2012-09-03",
            125: "2012-09-04",
            126: "2012-09-05",
            127: "2012-09-06",
            128: "2012-09-07",
            129: "2012-09-08",
            130: "2012-09-09",
            131: "2012-09-10",
            132: "2012-09-11",
            133: "2012-09-12",
            134: "2012-09-13",
            135: "2012-09-14",
            136: "2012-09-15",
            137: "2012-09-16",
            138: "2012-09-17",
            139: "2012-09-18",
            140: "2012-09-19",
            141: "2012-09-20",
            142: "2012-09-21",
            143: "2012-09-22",
            144: "2012-09-23",
            145: "2012-09-24",
            146: "2012-09-25",
            147: "2012-09-26",
            148: "2012-09-27",
            149: "2012-09-28",
            150: "2012-09-29",
            151: "2012-09-30",
            152: "2012-10-01",
            153: "2012-10-02",
            154: "2012-10-03",
            155: "2012-10-04",
            156: "2012-10-05",
            157: "2012-10-06",
            158: "2012-10-07",
            159: "2012-10-08",
            160: "2012-10-09",
            161: "2012-10-10",
            162: "2012-10-11",
            163: "2012-10-12",
            164: "2012-10-13",
            165: "2012-10-14",
            166: "2012-10-15",
            167: "2012-10-16",
            168: "2012-10-17",
            169: "2012-10-18",
            170: "2012-10-19",
            171: "2012-10-20",
            172: "2012-10-21",
            173: "2012-10-22",
            174: "2012-10-23",
            175: "2012-10-24",
            176: "2012-10-25",
            177: "2012-10-26",
            178: "2012-10-27",
            179: "2012-10-28",
            180: "2012-10-29",
            181: "2012-10-30",
            182: "2012-10-31",
            183: "2012-11-01",
            184: "2012-11-02",
            185: "2012-11-03",
            186: "2012-11-04",
            187: "2012-11-05",
            188: "2012-11-06",
            189: "2012-11-07",
            190: "2012-11-08",
            191: "2012-11-09",
            192: "2012-11-10",
            193: "2012-11-11",
            194: "2012-11-12",
            195: "2012-11-13",
            196: "2012-11-14",
            197: "2012-11-15",
            198: "2012-11-16",
            199: "2012-11-17",
            200: "2012-11-18",
            201: "2012-11-19",
            202: "2012-11-20",
            203: "2012-11-21",
            204: "2012-11-22",
            205: "2012-11-23",
            206: "2012-11-24",
            207: "2012-11-25",
            208: "2012-11-26",
            209: "2012-11-27",
            210: "2012-11-28",
            211: "2012-11-29",
            212: "2012-11-30",
            213: "2012-12-01",
            214: "2012-12-02",
            215: "2012-12-03",
            216: "2012-12-04",
            217: "2012-12-05",
            218: "2012-12-06",
            219: "2012-12-07",
            220: "2012-12-08",
            221: "2012-12-09",
            222: "2012-12-10",
            223: "2012-12-11",
            224: "2012-12-12",
            225: "2012-12-13",
            226: "2012-12-14",
            227: "2012-12-15",
            228: "2012-12-16",
            229: "2012-12-17",
            230: "2012-12-18",
            231: "2012-12-19",
            232: "2012-12-20",
            233: "2012-12-21",
            234: "2012-12-22",
            235: "2012-12-23",
            236: "2012-12-24",
            237: "2012-12-25",
            238: "2012-12-26",
            239: "2012-12-27",
            240: "2012-12-28",
            241: "2012-12-29",
            242: "2012-12-30",
            243: "2012-12-31",
            244: "2013-01-01",
            245: "2013-01-02",
            246: "2013-01-03",
            247: "2013-01-04",
            248: "2013-01-05",
            249: "2013-01-06",
            250: "2013-01-07",
            251: "2013-01-08",
            252: "2013-01-09",
            253: "2013-01-10",
            254: "2013-01-11",
            255: "2013-01-12",
            256: "2013-01-13",
            257: "2013-01-14",
            258: "2013-01-15",
            259: "2013-01-16",
            260: "2013-01-17",
            261: "2013-01-18",
            262: "2013-01-19",
            263: "2013-01-20",
            264: "2013-01-21",
            265: "2013-01-22",
            266: "2013-01-23",
            267: "2013-01-24",
            268: "2013-01-25",
            269: "2013-01-26",
            270: "2013-01-27",
            271: "2013-01-28",
            272: "2013-01-29",
            273: "2013-01-30",
            274: "2013-01-31",
            275: "2013-02-01",
            276: "2013-02-02",
            277: "2013-02-03",
            278: "2013-02-04",
            279: "2013-02-05",
            280: "2013-02-06",
            281: "2013-02-07",
            282: "2013-02-08",
            283: "2013-02-09",
            284: "2013-02-10",
            285: "2013-02-11",
            286: "2013-02-12",
            287: "2013-02-13",
            288: "2013-02-14",
            289: "2013-02-15",
            290: "2013-02-16",
            291: "2013-02-17",
            292: "2013-02-18",
            293: "2013-02-19",
            294: "2013-02-20",
            295: "2013-02-21",
            296: "2013-02-22",
            297: "2013-02-23",
            298: "2013-02-24",
            299: "2013-02-25",
            300: "2013-02-26",
            301: "2013-02-27",
            302: "2013-02-28",
            303: "2013-03-01",
            304: "2013-03-02",
            305: "2013-03-03",
            306: "2013-03-04",
            307: "2013-03-05",
            308: "2013-03-06",
            309: "2013-03-07",
            310: "2013-03-08",
            311: "2013-03-09",
            312: "2013-03-10",
            313: "2013-03-11",
            314: "2013-03-12",
            315: "2013-03-13",
            316: "2013-03-14",
            317: "2013-03-15",
            318: "2013-03-16",
            319: "2013-03-17",
            320: "2013-03-18",
            321: "2013-03-19",
            322: "2013-03-20",
            323: "2013-03-21",
            324: "2013-03-22",
            325: "2013-03-23",
            326: "2013-03-24",
            327: "2013-03-25",
            328: "2013-03-26",
            329: "2013-03-27",
            330: "2013-03-28",
            331: "2013-03-29",
            332: "2013-03-30",
            333: "2013-03-31",
            334: "2013-04-01",
            335: "2013-04-02",
            336: "2013-04-03",
            337: "2013-04-04",
            338: "2013-04-05",
            339: "2013-04-06",
            340: "2013-04-07",
            341: "2013-04-08",
            342: "2013-04-09",
            343: "2013-04-10",
            344: "2013-04-11",
            345: "2013-04-12",
            346: "2013-04-13",
            347: "2013-04-14",
            348: "2013-04-15",
            349: "2013-04-16",
            350: "2013-04-17",
            351: "2013-04-18",
            352: "2013-04-19",
            353: "2013-04-20",
            354: "2013-04-21",
            355: "2013-04-22",
            356: "2013-04-23",
            357: "2013-04-24",
            358: "2013-04-25",
            359: "2013-04-26",
            360: "2013-04-27",
            361: "2013-04-28",
            362: "2013-04-29",
            363: "2013-04-30",
        },
        "y": {
            0: np.nan,
            1: np.nan,
            2: np.nan,
            3: np.nan,
            4: np.nan,
            5: np.nan,
            6: np.nan,
            7: np.nan,
            8: np.nan,
            9: np.nan,
            10: np.nan,
            11: np.nan,
            12: np.nan,
            13: np.nan,
            14: np.nan,
            15: np.nan,
            16: np.nan,
            17: np.nan,
            18: np.nan,
            19: np.nan,
            20: np.nan,
            21: np.nan,
            22: np.nan,
            23: np.nan,
            24: np.nan,
            25: np.nan,
            26: np.nan,
            27: np.nan,
            28: np.nan,
            29: np.nan,
            30: np.nan,
            31: np.nan,
            32: np.nan,
            33: np.nan,
            34: np.nan,
            35: np.nan,
            36: np.nan,
            37: np.nan,
            38: np.nan,
            39: np.nan,
            40: np.nan,
            41: np.nan,
            42: np.nan,
            43: np.nan,
            44: np.nan,
            45: np.nan,
            46: np.nan,
            47: np.nan,
            48: np.nan,
            49: np.nan,
            50: np.nan,
            51: np.nan,
            52: np.nan,
            53: np.nan,
            54: np.nan,
            55: np.nan,
            56: np.nan,
            57: np.nan,
            58: np.nan,
            59: np.nan,
            60: np.nan,
            61: np.nan,
            62: np.nan,
            63: np.nan,
            64: np.nan,
            65: np.nan,
            66: np.nan,
            67: np.nan,
            68: np.nan,
            69: np.nan,
            70: np.nan,
            71: np.nan,
            72: np.nan,
            73: np.nan,
            74: np.nan,
            75: np.nan,
            76: np.nan,
            77: np.nan,
            78: np.nan,
            79: np.nan,
            80: np.nan,
            81: np.nan,
            82: np.nan,
            83: np.nan,
            84: np.nan,
            85: np.nan,
            86: np.nan,
            87: np.nan,
            88: np.nan,
            89: np.nan,
            90: np.nan,
            91: np.nan,
            92: np.nan,
            93: np.nan,
            94: np.nan,
            95: np.nan,
            96: np.nan,
            97: np.nan,
            98: np.nan,
            99: np.nan,
            100: np.nan,
            101: np.nan,
            102: np.nan,
            103: np.nan,
            104: np.nan,
            105: np.nan,
            106: np.nan,
            107: np.nan,
            108: np.nan,
            109: np.nan,
            110: np.nan,
            111: np.nan,
            112: np.nan,
            113: np.nan,
            114: np.nan,
            115: np.nan,
            116: np.nan,
            117: np.nan,
            118: np.nan,
            119: np.nan,
            120: np.nan,
            121: np.nan,
            122: np.nan,
            123: np.nan,
            124: np.nan,
            125: np.nan,
            126: np.nan,
            127: np.nan,
            128: np.nan,
            129: np.nan,
            130: np.nan,
            131: np.nan,
            132: np.nan,
            133: np.nan,
            134: np.nan,
            135: np.nan,
            136: np.nan,
            137: np.nan,
            138: np.nan,
            139: np.nan,
            140: np.nan,
            141: np.nan,
            142: np.nan,
            143: np.nan,
            144: np.nan,
            145: np.nan,
            146: np.nan,
            147: np.nan,
            148: np.nan,
            149: np.nan,
            150: np.nan,
            151: np.nan,
            152: np.nan,
            153: np.nan,
            154: np.nan,
            155: np.nan,
            156: np.nan,
            157: np.nan,
            158: np.nan,
            159: np.nan,
            160: np.nan,
            161: np.nan,
            162: np.nan,
            163: np.nan,
            164: np.nan,
            165: np.nan,
            166: np.nan,
            167: np.nan,
            168: np.nan,
            169: np.nan,
            170: np.nan,
            171: np.nan,
            172: np.nan,
            173: np.nan,
            174: np.nan,
            175: np.nan,
            176: np.nan,
            177: np.nan,
            178: np.nan,
            179: np.nan,
            180: np.nan,
            181: np.nan,
            182: np.nan,
            183: np.nan,
            184: np.nan,
            185: np.nan,
            186: np.nan,
            187: np.nan,
            188: np.nan,
            189: np.nan,
            190: np.nan,
            191: np.nan,
            192: np.nan,
            193: np.nan,
            194: np.nan,
            195: np.nan,
            196: np.nan,
            197: np.nan,
            198: np.nan,
            199: np.nan,
            200: np.nan,
            201: np.nan,
            202: np.nan,
            203: np.nan,
            204: np.nan,
            205: np.nan,
            206: np.nan,
            207: np.nan,
            208: np.nan,
            209: np.nan,
            210: np.nan,
            211: np.nan,
            212: np.nan,
            213: np.nan,
            214: np.nan,
            215: np.nan,
            216: np.nan,
            217: np.nan,
            218: np.nan,
            219: np.nan,
            220: np.nan,
            221: np.nan,
            222: np.nan,
            223: np.nan,
            224: np.nan,
            225: np.nan,
            226: np.nan,
            227: np.nan,
            228: np.nan,
            229: np.nan,
            230: np.nan,
            231: np.nan,
            232: np.nan,
            233: np.nan,
            234: np.nan,
            235: np.nan,
            236: np.nan,
            237: np.nan,
            238: np.nan,
            239: np.nan,
            240: np.nan,
            241: np.nan,
            242: np.nan,
            243: np.nan,
            244: np.nan,
            245: np.nan,
            246: np.nan,
            247: np.nan,
            248: np.nan,
            249: np.nan,
            250: np.nan,
            251: np.nan,
            252: np.nan,
            253: np.nan,
            254: np.nan,
            255: np.nan,
            256: np.nan,
            257: np.nan,
            258: np.nan,
            259: np.nan,
            260: np.nan,
            261: np.nan,
            262: np.nan,
            263: np.nan,
            264: np.nan,
            265: np.nan,
            266: np.nan,
            267: np.nan,
            268: np.nan,
            269: np.nan,
            270: np.nan,
            271: np.nan,
            272: np.nan,
            273: np.nan,
            274: np.nan,
            275: np.nan,
            276: np.nan,
            277: np.nan,
            278: np.nan,
            279: np.nan,
            280: np.nan,
            281: np.nan,
            282: np.nan,
            283: np.nan,
            284: np.nan,
            285: np.nan,
            286: np.nan,
            287: np.nan,
            288: np.nan,
            289: np.nan,
            290: np.nan,
            291: np.nan,
            292: np.nan,
            293: np.nan,
            294: np.nan,
            295: np.nan,
            296: np.nan,
            297: np.nan,
            298: np.nan,
            299: np.nan,
            300: np.nan,
            301: np.nan,
            302: np.nan,
            303: np.nan,
            304: np.nan,
            305: np.nan,
            306: np.nan,
            307: np.nan,
            308: np.nan,
            309: np.nan,
            310: np.nan,
            311: np.nan,
            312: np.nan,
            313: np.nan,
            314: np.nan,
            315: np.nan,
            316: np.nan,
            317: np.nan,
            318: np.nan,
            319: np.nan,
            320: np.nan,
            321: np.nan,
            322: np.nan,
            323: np.nan,
            324: np.nan,
            325: np.nan,
            326: np.nan,
            327: np.nan,
            328: np.nan,
            329: np.nan,
            330: np.nan,
            331: np.nan,
            332: np.nan,
            333: np.nan,
            334: np.nan,
            335: np.nan,
            336: np.nan,
            337: np.nan,
            338: np.nan,
            339: np.nan,
            340: np.nan,
            341: np.nan,
            342: np.nan,
            343: np.nan,
            344: np.nan,
            345: np.nan,
            346: np.nan,
            347: np.nan,
            348: np.nan,
            349: np.nan,
            350: np.nan,
            351: np.nan,
            352: np.nan,
            353: np.nan,
            354: np.nan,
            355: np.nan,
            356: np.nan,
            357: np.nan,
            358: np.nan,
            359: np.nan,
            360: np.nan,
            361: np.nan,
            362: np.nan,
            363: np.nan,
        },
    }
)

METALEARNING_TEST_T1 = pd.DataFrame(
    {
        "time": {i: t for i, t in enumerate(pd.date_range("2021-05-06", periods=60))},
        "value": {
            0: 0.7112185692244729,
            1: 0.28612756331913936,
            2: 1.7908691322641883,
            3: 0.43736075519156936,
            4: -0.23525058548224762,
            5: -1.7582001930868774,
            6: -1.3365811786052153,
            7: 2.70136827387631,
            8: -0.8503288703120376,
            9: -0.47291541289255906,
            10: -0.9736036599402202,
            11: -0.053613618660455685,
            12: -0.4399764354493508,
            13: 0.6510891719510783,
            14: 0.9102880748521228,
            15: -1.2169517244324117,
            16: 0.8823261711758554,
            17: -0.5167547199636685,
            18: -0.4241112420820669,
            19: -0.15161410456893973,
            20: 1.6557873470589026,
            21: 1.2492635567473669,
            22: 0.07067834347638169,
            23: 0.3623930917415304,
            24: -0.32136739683067467,
            25: 0.9965947640417286,
            26: 0.8934515245219763,
            27: 1.3250787764049077,
            28: 1.2944296521273506,
            29: -1.3910010840943103,
            30: 0.6328473646452667,
            31: -0.6151063175871002,
            32: 0.8221900526926592,
            33: 0.8837000910837177,
            34: 0.6241905424412373,
            35: -0.8085562684125828,
            36: -0.3968679453028329,
            37: 0.6234364586120968,
            38: -0.37072070379376093,
            39: -1.7565683865929325,
            40: 1.9080129722528856,
            41: -1.5900532052259917,
            42: -1.5547053773517148,
            43: 0.6241855745492039,
            44: -1.4985540880236143,
            45: -0.6693668595230223,
            46: -1.9413572006241901,
            47: 1.1699716086421006,
            48: -0.2929609945390526,
            49: 0.8411729895364164,
            50: -0.7087415808122113,
            51: 1.2840790235360602,
            52: -0.7147023141176251,
            53: 1.7370410717756741,
            54: -0.5205749206419553,
            55: -0.15020971601450228,
            56: -1.5088757646035433,
            57: -1.043191636587259,
            58: 0.09474263159788751,
            59: -0.12799293830733294,
        },
    }
)

METALEARNING_TEST_T1_FEATURES: Dict[str, float] = {
    "length": 60,
    "mean": 0.017541978414630434,
    "var": 1.1330423233556002,
    "entropy": 0.8859834841922064,
    "lumpiness": 0.02715210543626642,
    "stability": 0.040424748165751194,
    "flat_spots": 1,
    "hurst": -0.009818911425943738,
    "std1st_der": 0.7210367489651559,
    "crossing_points": 32,
    "binarize_mean": 0.4666666666666667,
    "unitroot_kpss": 0.07430358370741166,
    "heterogeneity": 6.697453093870143,
    "histogram_mode": -0.5485395582740402,
    "linearity": 0.02245331522304122,
    "trend_strength": 0.22455457938231527,
    "seasonality_strength": 0.4810375570834332,
    "spikiness": 0.00012961684067190377,
    "peak": 0,
    "trough": 5,
    "level_shift_idx": 21,
    "level_shift_size": 0.1419658380986679,
    "y_acf1": -0.1334061337054278,
    "y_acf5": 0.03813891322368813,
    "diff1y_acf1": -0.5958571156764788,
    "diff1y_acf5": 0.4160058545847156,
    "diff2y_acf1": -0.7374357720433103,
    "diff2y_acf5": 0.7048949611179683,
    "y_pacf5": 0.03335133884678569,
    "diff1y_pacf5": 0.7499500138901602,
    "diff2y_pacf5": 1.8304505333773173,
    "seas_acf1": 0.16081850213286256,
    "seas_pacf1": 0.22189491849213927,
    "firstzero_ac": 3,
    "holt_alpha": 0.21052631578947367,
    "holt_beta": 0.21052631578947367,
    "hw_alpha": np.nan,
    "hw_beta": np.nan,
    "hw_gamma": np.nan,
}

METALEARNING_TEST_T2 = pd.DataFrame(
    {
        "time": {i: t for i, t in enumerate(pd.date_range("2021-05-06", periods=60))},
        "value": {
            0: 0.03311787628295211,
            1: 0.5273022473907714,
            2: 0.3773553931583247,
            3: 0.09563694333627344,
            4: 1.524706793895165,
            5: 0.6171562805357564,
            6: 0.35859880554321116,
            7: 0.6050713159814466,
            8: 0.8843087863778628,
            9: 1.6375480266541607,
            10: 0.7061190560580963,
            11: 0.6961083764757605,
            12: 0.05859116425445711,
            13: 1.4199818522703387,
            14: 0.14449318312962062,
            15: 3.139712107794079,
            16: 1.8761537803231672,
            17: 0.14085831327945778,
            18: 1.7274097408558717,
            19: 0.042350555351393815,
            20: 1.950774376187714,
            21: 1.062863664255698,
            22: 0.8975406475971797,
            23: 0.7556289030342478,
            24: 1.193215581899301,
            25: 0.7265814392991706,
            26: 0.7091637521237201,
            27: 0.6387444272009849,
            28: 0.6724117330778265,
            29: 1.2893706824976368,
            30: 0.39742659040034645,
            31: 1.595405033245209,
            32: 0.7458532366189279,
            33: 0.7032615006875266,
            34: 0.5933185716755207,
            35: 0.16107480444349953,
            36: 0.5592223806594153,
            37: 0.7745859398941953,
            38: 1.3068416600591384,
            39: 1.2436098268331073,
            40: 0.25952021498932615,
            41: 1.347500758310904,
            42: 1.8494084696277076,
            43: 0.3054109812345139,
            44: 0.6470355749547894,
            45: 0.21825776026063276,
            46: 0.36770844840170835,
            47: 0.7560966411821854,
            48: 0.16415990275685444,
            49: 0.552571928952884,
            50: 2.04178068255798,
            51: 1.790573925858621,
            52: 0.24618139328935654,
            53: 0.400888337382867,
            54: 2.175527336804398,
            55: 1.406341482682491,
            56: 1.0197034851735676,
            57: 1.9823165173659234,
            58: 0.13775080596032568,
            59: 1.081401230949507,
        },
    }
)

# pyre-fixme[5]: Global expression must be annotated.
METALEARNING_TEST_T2_FEATURES = {
    "length": 60,
    "mean": 0.8889935204889182,
    "var": 0.4339723115044476,
    "entropy": 0.9062662461791416,
    "lumpiness": 0.023362172264992747,
    "stability": 0.030229530493845646,
    "flat_spots": 1,
    "hurst": -0.021772668163519544,
    "std1st_der": 0.48397243746464197,
    "crossing_points": 27,
    "binarize_mean": 0.38333333333333336,
    "unitroot_kpss": 0.08053956294618499,
    "heterogeneity": 3.732058275703587,
    "histogram_mode": 0.03311787628295211,
    "linearity": 0.012797310585104091,
    "trend_strength": 0.24190571668002014,
    "seasonality_strength": 0.37385052827383813,
    "spikiness": 2.8368797395354512e-05,
    "peak": 4,
    "trough": 3,
    "level_shift_idx": 15,
    "level_shift_size": 0.14893186516752932,
    "y_acf1": -0.09313831054944781,
    "y_acf5": 0.041934017014729597,
    "diff1y_acf1": -0.5279995758182909,
    "diff1y_acf5": 0.3441814241353133,
    "diff2y_acf1": -0.6592266744380927,
    "diff2y_acf5": 0.5333616344338944,
    "y_pacf5": 0.042648851746986086,
    "diff1y_pacf5": 0.6799034767926148,
    "diff2y_pacf5": 1.2927400320509825,
    "seas_acf1": 0.00474904222956029,
    "seas_pacf1": 0.015773034095832947,
    "firstzero_ac": 4,
    "holt_alpha": 0.20073207666635207,
    "holt_beta": 0.20073038111605043,
    "hw_alpha": 0.10526241886359299,
    "hw_beta": 0.10526943965945956,
    "hw_gamma": 0.26315604715898244,
}

# pyre-fixme[5]: Global expression must be annotated.
METALEARNING_TEST_FEATURES = [
    [
        1.08565103,
        -0.92567252,
        -0.31020792,
        1.29803874,
        -0.80276567,
        -0.26583506,
        -2.83965074,
        -0.53971508,
        -0.52822807,
        0.23892437,
        -0.13409521,
        -0.44432967,
        -0.16805101,
        -0.90482502,
        -0.02182439,
        -0.11112191,
        -0.48058064,
        0.90641584,
        0.73888397,
        0.49253603,
        -1.0450474,
        1.15928987,
        -0.60442358,
        2.40575244,
        -1.9539288,
        0.4424955,
        -1.34667628,
        -1.30152228,
        -0.69201056,
        0.63910397,
        0.21705932,
        1.36665286,
        -0.59686006,
        -0.11579573,
        0.43509245,
        0.92586754,
        -0.10588535,
        0.23006655,
        0.9286056,
    ],
    [
        -0.05468981,
        0.14851362,
        -0.77330589,
        0.09830413,
        -0.4989081,
        -0.83200877,
        0.95829231,
        0.6187273,
        0.36315383,
        0.36637462,
        0.39614439,
        -0.2464249,
        -1.6899136,
        1.41230063,
        -1.22986175,
        0.07146394,
        -0.22278461,
        -0.24202289,
        -1.41214979,
        -0.34357369,
        -0.74921607,
        -0.31704973,
        0.23464001,
        -1.1360718,
        0.39051003,
        0.52022306,
        -0.80449176,
        0.53360451,
        0.39917439,
        -1.29757766,
        -0.96291282,
        -0.89634859,
        0.47413525,
        0.41800094,
        -0.50182222,
        1.00137551,
        -0.78192466,
        0.27105568,
        -0.53869405,
    ],
]

METALEARNING_TEST_MULTI = pd.DataFrame(
    {
        "time": {i: t for i, t in enumerate(pd.date_range("2021-05-06", periods=30))},
        "y": {
            0: 1,
            1: 2,
            2: 3,
            3: 4,
            4: 5,
            5: 6,
            6: 7,
            7: 8,
            8: 9,
            9: 10,
            10: 11,
            11: 12,
            12: 13,
            13: 14,
            14: 15,
            15: 16,
            16: 17,
            17: 18,
            18: 19,
            19: 20,
            20: 21,
            21: 22,
            22: 23,
            23: 24,
            24: 25,
            25: 26,
            26: 27,
            27: 28,
            28: 29,
            29: 30,
        },
        "z": {
            0: 1.2622887024442238,
            1: -1.375388419424237,
            2: 1.6105001100909309,
            3: 0.8360217153674283,
            4: -1.1787646903164346,
            5: -0.4873760750756692,
            6: -1.385672390891951,
            7: 0.6208331955845247,
            8: 0.7488032806815271,
            9: 0.3002392127702183,
            10: -0.22645033854916954,
            11: -0.9209664873600163,
            12: 0.07008998004246421,
            13: -0.6716084779303537,
            14: 0.47582398344006577,
            15: -0.17841450908106507,
            16: 0.0905434173978501,
            17: -0.10524527869658624,
            18: -0.23408569091546855,
            19: -1.0165202742858588,
            20: 0.8245635894080147,
            21: 0.7619909485066353,
            22: 0.2854745931462472,
            23: -0.8794144092785487,
            24: 1.294518927777081,
            25: -0.1593423664921398,
            26: -0.25588735974394616,
            27: -1.1383373628596727,
            28: -0.7418581263160675,
            29: -0.28032496781238847,
        },
    }
)

METALEARNING_TEST_METADATA_STR: List[Dict[str, str]] = [
    {
        "hpt_res": "{'arima': ({}, 0.8)}",
        "features": "{'f': 1.0}",
        "best_model": "arima",
    },
    {
        "hpt_res": "{'stlf': ({}, 0.1)}",
        "features": "{'f': 1.0}",
        "best_model": "stlf",
    },
]

PEYTON_FCST_15_ARIMA_PARAM_1_MODEL_1 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
        },
        "fcst": {
            0: 7.940480857230361,
            1: 7.890521247867683,
            2: 7.868743227314358,
            3: 7.859001067790166,
            4: 7.854399212870253,
            5: 7.851992691658566,
            6: 7.850523758870425,
            7: 7.849455253611216,
            8: 7.848557763908408,
            9: 7.847733311942562,
            10: 7.846940053109861,
            11: 7.84616011631582,
            12: 7.845385869130233,
            13: 7.844614051876368,
            14: 7.843843272403614,
        },
        "fcst_lower": {
            0: 7.05954539696271,
            1: 6.895622219348986,
            2: 6.834214576608463,
            3: 6.803917944580299,
            4: 6.784960481789183,
            5: 6.770545536610528,
            6: 6.758058474764527,
            7: 6.746435277314804,
            8: 6.7352309284064615,
            9: 6.7242563949242085,
            10: 6.71343022358396,
            11: 6.702716519862747,
            12: 6.692098772048167,
            13: 6.681568754691981,
            14: 6.671121814034725,
        },
        "fcst_upper": {
            0: 8.821416317498013,
            1: 8.88542027638638,
            2: 8.903271878020252,
            3: 8.914084191000033,
            4: 8.923837943951323,
            5: 8.933439846706605,
            6: 8.942989042976322,
            7: 8.952475229907629,
            8: 8.961884599410356,
            9: 8.971210228960917,
            10: 8.980449882635762,
            11: 8.989603712768893,
            12: 8.998672966212299,
            13: 9.007659349060756,
            14: 9.016564730772503,
        },
    }
)

PEYTON_FCST_30_ARIMA_PARAM_1_MODEL_1 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.940480857230361,
            1: 7.890521247867683,
            2: 7.868743227314358,
            3: 7.859001067790166,
            4: 7.854399212870253,
            5: 7.851992691658566,
            6: 7.850523758870425,
            7: 7.849455253611216,
            8: 7.848557763908408,
            9: 7.847733311942562,
            10: 7.846940053109861,
            11: 7.84616011631582,
            12: 7.845385869130233,
            13: 7.844614051876368,
            14: 7.843843272403614,
            15: 7.843072936148924,
            16: 7.842302789184875,
            17: 7.841532723063531,
            18: 7.840762691468684,
            19: 7.839992674619497,
            20: 7.839222664067922,
            21: 7.838452656205947,
            22: 7.837682649492652,
            23: 7.836912643269938,
            24: 7.836142637256742,
            25: 7.835372631333029,
            26: 7.834602625447531,
            27: 7.833832619578356,
            28: 7.833062613716151,
            29: 7.832292607856923,
        },
        "fcst_lower": {
            0: 7.05954539696271,
            1: 6.895622219348986,
            2: 6.834214576608463,
            3: 6.803917944580299,
            4: 6.784960481789183,
            5: 6.770545536610528,
            6: 6.758058474764527,
            7: 6.746435277314804,
            8: 6.7352309284064615,
            9: 6.7242563949242085,
            10: 6.71343022358396,
            11: 6.702716519862747,
            12: 6.692098772048167,
            13: 6.681568754691981,
            14: 6.671121814034725,
            15: 6.660754861687368,
            16: 6.65046551906174,
            17: 6.6402517511320145,
            18: 6.630111708424872,
            19: 6.620043657716803,
            20: 6.610045950601637,
            21: 6.600117008296207,
            22: 6.590255313472834,
            23: 6.580459405190895,
            24: 6.570727875248046,
            25: 6.561059365228859,
            26: 6.5514525639365075,
            27: 6.541906205067343,
            28: 6.532419065062822,
            29: 6.522989961105574,
        },
        "fcst_upper": {
            0: 8.821416317498013,
            1: 8.88542027638638,
            2: 8.903271878020252,
            3: 8.914084191000033,
            4: 8.923837943951323,
            5: 8.933439846706605,
            6: 8.942989042976322,
            7: 8.952475229907629,
            8: 8.961884599410356,
            9: 8.971210228960917,
            10: 8.980449882635762,
            11: 8.989603712768893,
            12: 8.998672966212299,
            13: 9.007659349060756,
            14: 9.016564730772503,
            15: 9.02539101061048,
            16: 9.03414005930801,
            17: 9.042813694995047,
            18: 9.051413674512496,
            19: 9.05994169152219,
            20: 9.068399377534208,
            21: 9.076788304115686,
            22: 9.085109985512469,
            23: 9.09336588134898,
            24: 9.101557399265438,
            25: 9.109685897437199,
            26: 9.117752686958555,
            27: 9.125759034089368,
            28: 9.13370616236948,
            29: 9.141595254608273,
        },
    }
)

PEYTON_FCST_15_ARIMA_PARAM_2_MODEL_1 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
        },
        "fcst": {
            0: 7.881135373636217,
            1: 7.834420797133395,
            2: 7.8666993351876275,
            3: 7.852679933930919,
            4: 7.857141058696803,
            5: 7.85422540066944,
            6: 7.854254297592095,
            7: 7.853107830660793,
            8: 7.852430528072707,
            9: 7.851565951407573,
            10: 7.850776128036123,
            11: 7.849956465751542,
            12: 7.849148714122571,
            13: 7.848336208174419,
            14: 7.847525599985015,
        },
        "fcst_lower": {
            0: 7.016738678610501,
            1: 6.820382617649829,
            2: 6.827476584820522,
            3: 6.8060466501466355,
            4: 6.7975082882872675,
            5: 6.784161505481881,
            6: 6.772936132884821,
            7: 6.761020962803888,
            8: 6.749534723245571,
            9: 6.738023710887118,
            10: 6.7266655269502795,
            11: 6.715384943627649,
            12: 6.704208284786378,
            13: 6.693121344934464,
            14: 6.682126323863031,
        },
        "fcst_upper": {
            0: 8.745532068661932,
            1: 8.84845897661696,
            2: 8.905922085554733,
            3: 8.899313217715202,
            4: 8.91677382910634,
            5: 8.924289295857,
            6: 8.935572462299369,
            7: 8.945194698517698,
            8: 8.955326332899844,
            9: 8.965108191928028,
            10: 8.974886729121966,
            11: 8.984527987875435,
            12: 8.994089143458766,
            13: 9.003551071414375,
            14: 9.012924876106998,
        },
    }
)

PEYTON_FCST_30_ARIMA_PARAM_2_MODEL_1 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.881135373636217,
            1: 7.834420797133395,
            2: 7.8666993351876275,
            3: 7.852679933930919,
            4: 7.857141058696803,
            5: 7.85422540066944,
            6: 7.854254297592095,
            7: 7.853107830660793,
            8: 7.852430528072707,
            9: 7.851565951407573,
            10: 7.850776128036123,
            11: 7.849956465751542,
            12: 7.849148714122571,
            13: 7.848336208174419,
            14: 7.847525599985015,
            15: 7.846714234276368,
            16: 7.845903170943038,
            17: 7.8450919869120055,
            18: 7.844280851059295,
            19: 7.843469695975475,
            20: 7.842658548568045,
            21: 7.841847398096467,
            22: 7.841036248847991,
            23: 7.840225099111295,
            24: 7.8394139495694795,
            25: 7.838602799949874,
            26: 7.83779165036132,
            27: 7.836980500760371,
            28: 7.8361693511643695,
            29: 7.835358201566393,
        },
        "fcst_lower": {
            0: 7.016738678610501,
            1: 6.820382617649829,
            2: 6.827476584820522,
            3: 6.8060466501466355,
            4: 6.7975082882872675,
            5: 6.784161505481881,
            6: 6.772936132884821,
            7: 6.761020962803888,
            8: 6.749534723245571,
            9: 6.738023710887118,
            10: 6.7266655269502795,
            11: 6.715384943627649,
            12: 6.704208284786378,
            13: 6.693121344934464,
            14: 6.682126323863031,
            15: 6.671218999579241,
            16: 6.660397866112156,
            17: 6.649660463925119,
            18: 6.639004842214219,
            19: 6.628428965925433,
            20: 6.617930946560942,
            21: 6.607508943515154,
            22: 6.59716119779362,
            23: 6.586886013117941,
            24: 6.576681758463545,
            25: 6.566546862351812,
            26: 6.556479810751995,
            27: 6.546479143822264,
            28: 6.536543453377417,
            29: 6.526671380307525,
        },
        "fcst_upper": {
            0: 8.745532068661932,
            1: 8.84845897661696,
            2: 8.905922085554733,
            3: 8.899313217715202,
            4: 8.91677382910634,
            5: 8.924289295857,
            6: 8.935572462299369,
            7: 8.945194698517698,
            8: 8.955326332899844,
            9: 8.965108191928028,
            10: 8.974886729121966,
            11: 8.984527987875435,
            12: 8.994089143458766,
            13: 9.003551071414375,
            14: 9.012924876106998,
            15: 9.022209468973495,
            16: 9.03140847577392,
            17: 9.040523509898893,
            18: 9.04955685990437,
            19: 9.058510426025517,
            20: 9.067386150575148,
            21: 9.07618585267778,
            22: 9.084911299902362,
            23: 9.093564185104649,
            24: 9.102146140675414,
            25: 9.110658737547936,
            26: 9.119103489970644,
            27: 9.127481857698477,
            28: 9.135795248951322,
            29: 9.14404502282526,
        },
    }
)

PEYTON_FCST_15_ARIMA_PARAM_1_MODEL_2 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
        },
        "fcst": {
            0: 7.944025543438113,
            1: 7.896058661864416,
            2: 7.875588940590136,
            3: 7.866853549101262,
            4: 7.863125747269404,
            5: 7.861534919140486,
            6: 7.860856038059918,
            7: 7.860566327617177,
            8: 7.86044269456415,
            9: 7.8603899345336306,
            10: 7.860367419350577,
            11: 7.860357811064475,
            12: 7.860353710757513,
            13: 7.86035196096395,
            14: 7.860351214244852,
        },
        "fcst_lower": {
            0: 7.061827523068538,
            1: 6.90033565641836,
            2: 6.840623978834781,
            3: 6.811641639531865,
            4: 6.793819879993599,
            5: 6.7804584768342115,
            6: 6.768986367083855,
            7: 6.758358954743929,
            8: 6.748139882498416,
            9: 6.738144012554313,
            10: 6.728291682425065,
            11: 6.718547857384789,
            12: 6.708896471907462,
            13: 6.699329561180043,
            14: 6.689842647110949,
        },
        "fcst_upper": {
            0: 8.82622356380769,
            1: 8.891781667310472,
            2: 8.910553902345491,
            3: 8.922065458670659,
            4: 8.932431614545209,
            5: 8.942611361446762,
            6: 8.952725709035981,
            7: 8.962773700490425,
            8: 8.972745506629884,
            9: 8.982635856512948,
            10: 8.992443156276087,
            11: 9.00216776474416,
            12: 9.011810949607565,
            13: 9.021374360747856,
            14: 9.030859781378755,
        },
    }
)

PEYTON_FCST_30_ARIMA_PARAM_1_MODEL_2 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.944025543438113,
            1: 7.896058661864416,
            2: 7.875588940590136,
            3: 7.866853549101262,
            4: 7.863125747269404,
            5: 7.861534919140486,
            6: 7.860856038059918,
            7: 7.860566327617177,
            8: 7.86044269456415,
            9: 7.8603899345336306,
            10: 7.860367419350577,
            11: 7.860357811064475,
            12: 7.860353710757513,
            13: 7.86035196096395,
            14: 7.860351214244852,
            15: 7.860350895584741,
            16: 7.860350759597505,
            17: 7.860350701565364,
            18: 7.860350676800325,
            19: 7.860350666231919,
            20: 7.860350661721884,
            21: 7.86035065979724,
            22: 7.8603506589759045,
            23: 7.860350658625402,
            24: 7.860350658475825,
            25: 7.860350658411995,
            26: 7.860350658384755,
            27: 7.860350658373131,
            28: 7.86035065836817,
            29: 7.860350658366054,
        },
        "fcst_lower": {
            0: 7.061827523068538,
            1: 6.90033565641836,
            2: 6.840623978834781,
            3: 6.811641639531865,
            4: 6.793819879993599,
            5: 6.7804584768342115,
            6: 6.768986367083855,
            7: 6.758358954743929,
            8: 6.748139882498416,
            9: 6.738144012554313,
            10: 6.728291682425065,
            11: 6.718547857384789,
            12: 6.708896471907462,
            13: 6.699329561180043,
            14: 6.689842647110949,
            15: 6.680432776462899,
            16: 6.6710976850257735,
            17: 6.66183544024345,
            18: 6.652644287276451,
            19: 6.643522581653869,
            20: 6.634468758853449,
            21: 6.62548131969489,
            22: 6.616558822562795,
            23: 6.607699878632374,
            24: 6.598903148463457,
            25: 6.590167339261074,
            26: 6.581491202497958,
            27: 6.572873531763655,
            28: 6.564313160777465,
            29: 6.555808961533698,
        },
        "fcst_upper": {
            0: 8.82622356380769,
            1: 8.891781667310472,
            2: 8.910553902345491,
            3: 8.922065458670659,
            4: 8.932431614545209,
            5: 8.942611361446762,
            6: 8.952725709035981,
            7: 8.962773700490425,
            8: 8.972745506629884,
            9: 8.982635856512948,
            10: 8.992443156276087,
            11: 9.00216776474416,
            12: 9.011810949607565,
            13: 9.021374360747856,
            14: 9.030859781378755,
            15: 9.040269014706583,
            16: 9.049603834169236,
            17: 9.05886596288728,
            18: 9.068057066324197,
            19: 9.077178750809969,
            20: 9.086232564590318,
            21: 9.09521999989959,
            22: 9.104142495389015,
            23: 9.11300143861843,
            24: 9.121798168488194,
            25: 9.130533977562916,
            26: 9.139210114271552,
            27: 9.147827784982606,
            28: 9.156388155958876,
            29: 9.16489235519841,
        },
    }
)

PEYTON_FCST_15_ARIMA_PARAM_2_MODEL_2 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
        },
        "fcst": {
            0: 8.730436154070746,
            1: 1.7764696188918219,
            2: 1.7588528682367137,
            3: 1.7412361175816515,
            4: 1.7236193669266353,
            5: 1.706002616271665,
            6: 1.6883858656167408,
            7: 1.6707691149618622,
            8: 1.6531523643070296,
            9: 1.635535613652243,
            10: 1.6179188629975023,
            11: 1.6003021123428076,
            12: 1.5826853616881589,
            13: 1.565068611033556,
            14: 1.5474518603789993,
        },
        "fcst_lower": {
            0: 1.1291162054717665,
            1: -8.973407616092297,
            2: -8.991024366747405,
            3: -9.008641117402467,
            4: -9.026257868057483,
            5: -9.043874618712454,
            6: -9.061491369367378,
            7: -9.079108120022257,
            8: -9.09672487067709,
            9: -9.114341621331876,
            10: -9.131958371986617,
            11: -9.149575122641311,
            12: -9.16719187329596,
            13: -9.184808623950563,
            14: -9.20242537460512,
        },
        "fcst_upper": {
            0: 16.331756102669726,
            1: 12.52634685387594,
            2: 12.508730103220833,
            3: 12.49111335256577,
            4: 12.473496601910755,
            5: 12.455879851255784,
            6: 12.43826310060086,
            7: 12.420646349945981,
            8: 12.403029599291148,
            9: 12.385412848636362,
            10: 12.367796097981621,
            11: 12.350179347326927,
            12: 12.332562596672277,
            13: 12.314945846017675,
            14: 12.297329095363118,
        },
    }
)

PEYTON_FCST_30_ARIMA_PARAM_2_MODEL_2 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 8.730436154070746,
            1: 1.7764696188918219,
            2: 1.7588528682367137,
            3: 1.7412361175816515,
            4: 1.7236193669266353,
            5: 1.706002616271665,
            6: 1.6883858656167408,
            7: 1.6707691149618622,
            8: 1.6531523643070296,
            9: 1.635535613652243,
            10: 1.6179188629975023,
            11: 1.6003021123428076,
            12: 1.5826853616881589,
            13: 1.565068611033556,
            14: 1.5474518603789993,
            15: 1.5298351097244884,
            16: 1.5122183590700233,
            17: 1.4946016084156042,
            18: 1.476984857761231,
            19: 1.4593681071069038,
            20: 1.4417513564526225,
            21: 1.4241346057983872,
            22: 1.406517855144198,
            23: 1.3889011044900545,
            24: 1.3712843538359571,
            25: 1.3536676031819055,
            26: 1.3360508525278998,
            27: 1.31843410187394,
            28: 1.3008173512200263,
            29: 1.2832006005661585,
        },
        "fcst_lower": {
            0: 1.1291162054717665,
            1: -8.973407616092297,
            2: -8.991024366747405,
            3: -9.008641117402467,
            4: -9.026257868057483,
            5: -9.043874618712454,
            6: -9.061491369367378,
            7: -9.079108120022257,
            8: -9.09672487067709,
            9: -9.114341621331876,
            10: -9.131958371986617,
            11: -9.149575122641311,
            12: -9.16719187329596,
            13: -9.184808623950563,
            14: -9.20242537460512,
            15: -9.220042125259631,
            16: -9.237658875914097,
            17: -9.255275626568515,
            18: -9.272892377222888,
            19: -9.290509127877215,
            20: -9.308125878531497,
            21: -9.325742629185731,
            22: -9.34335937983992,
            23: -9.360976130494064,
            24: -9.378592881148162,
            25: -9.396209631802213,
            26: -9.41382638245622,
            27: -9.431443133110179,
            28: -9.449059883764093,
            29: -9.466676634417961,
        },
        "fcst_upper": {
            0: 16.331756102669726,
            1: 12.52634685387594,
            2: 12.508730103220833,
            3: 12.49111335256577,
            4: 12.473496601910755,
            5: 12.455879851255784,
            6: 12.43826310060086,
            7: 12.420646349945981,
            8: 12.403029599291148,
            9: 12.385412848636362,
            10: 12.367796097981621,
            11: 12.350179347326927,
            12: 12.332562596672277,
            13: 12.314945846017675,
            14: 12.297329095363118,
            15: 12.279712344708607,
            16: 12.262095594054141,
            17: 12.244478843399722,
            18: 12.22686209274535,
            19: 12.209245342091023,
            20: 12.191628591436741,
            21: 12.174011840782507,
            22: 12.156395090128317,
            23: 12.138778339474174,
            24: 12.121161588820076,
            25: 12.103544838166025,
            26: 12.085928087512018,
            27: 12.06831133685806,
            28: 12.050694586204145,
            29: 12.033077835550277,
        },
    }
)

PEYTON_FCST_15_ARIMA_PARAM_1_MODEL_1_INCL_HIST = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("2012-05-03 00:00:00"),
            "fcst": -0.0007700058570090651,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-04 00:00:00"),
            "fcst": 0.003945764176497557,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-05 00:00:00"),
            "fcst": 0.056389253940629255,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-06 00:00:00"),
            "fcst": 0.03581633435595588,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-07 00:00:00"),
            "fcst": -0.4128869117836815,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-08 00:00:00"),
            "fcst": -0.21494697202566299,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-09 00:00:00"),
            "fcst": 0.023753499987732296,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-10 00:00:00"),
            "fcst": 0.09355937802330405,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-11 00:00:00"),
            "fcst": 0.18319088827293034,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-12 00:00:00"),
            "fcst": 0.20044808320056756,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-13 00:00:00"),
            "fcst": 0.2503354760854294,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-14 00:00:00"),
            "fcst": 0.17127042819947244,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-15 00:00:00"),
            "fcst": 0.14608737892963222,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-16 00:00:00"),
            "fcst": 0.1046934406435516,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-17 00:00:00"),
            "fcst": 0.022584545049575466,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-18 00:00:00"),
            "fcst": 0.05981881271357159,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-19 00:00:00"),
            "fcst": 0.14025155875910666,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-20 00:00:00"),
            "fcst": 0.2620310138400047,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-21 00:00:00"),
            "fcst": 0.20643571244428782,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-22 00:00:00"),
            "fcst": -0.03163734777951549,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-23 00:00:00"),
            "fcst": -0.17638166890869264,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-24 00:00:00"),
            "fcst": -0.026283311915276913,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-25 00:00:00"),
            "fcst": 0.08888312964866367,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-26 00:00:00"),
            "fcst": 0.09318350600838077,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-27 00:00:00"),
            "fcst": 0.21884338795240824,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-28 00:00:00"),
            "fcst": 0.11943606160735805,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-29 00:00:00"),
            "fcst": 0.09954952209841111,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-30 00:00:00"),
            "fcst": 0.0115899772154675,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-31 00:00:00"),
            "fcst": 0.012880997922767479,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-01 00:00:00"),
            "fcst": -0.0728620311757119,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-02 00:00:00"),
            "fcst": 0.01827210829676365,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-03 00:00:00"),
            "fcst": 0.08966940872011074,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-04 00:00:00"),
            "fcst": 0.06304407979604659,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-05 00:00:00"),
            "fcst": -0.013443609595630263,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-06 00:00:00"),
            "fcst": -0.05624628223233899,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-07 00:00:00"),
            "fcst": 0.0829341015327528,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-08 00:00:00"),
            "fcst": 0.03996293101592904,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-09 00:00:00"),
            "fcst": -0.03873226464407914,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-10 00:00:00"),
            "fcst": -0.06431422815004223,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-11 00:00:00"),
            "fcst": 0.028090033076010767,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-12 00:00:00"),
            "fcst": 0.08447685368419985,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-13 00:00:00"),
            "fcst": -0.02645959470756161,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-14 00:00:00"),
            "fcst": -0.04747711263299492,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-15 00:00:00"),
            "fcst": 0.03990163053922584,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-16 00:00:00"),
            "fcst": 0.04021077078495444,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-17 00:00:00"),
            "fcst": 0.11798186890164153,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-18 00:00:00"),
            "fcst": -0.07080972833656002,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-19 00:00:00"),
            "fcst": -0.011919670865285226,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-20 00:00:00"),
            "fcst": -0.2962532999717047,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-21 00:00:00"),
            "fcst": -0.16324802300440683,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-22 00:00:00"),
            "fcst": 0.0036335041454830658,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-23 00:00:00"),
            "fcst": -0.02623718629423044,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-24 00:00:00"),
            "fcst": 0.14562991055130833,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-25 00:00:00"),
            "fcst": 0.15360465873927256,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-26 00:00:00"),
            "fcst": 0.07657127134706403,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-27 00:00:00"),
            "fcst": 0.01072068277829874,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-28 00:00:00"),
            "fcst": 0.08562704277501096,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-29 00:00:00"),
            "fcst": 0.0853670485997517,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-06-30 00:00:00"),
            "fcst": 0.10168388082429597,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-01 00:00:00"),
            "fcst": 0.18331939970107475,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-02 00:00:00"),
            "fcst": 0.10888859514445062,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-03 00:00:00"),
            "fcst": 0.073361421870783,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-04 00:00:00"),
            "fcst": 0.00505975073752761,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-05 00:00:00"),
            "fcst": -0.011786813571759813,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-06 00:00:00"),
            "fcst": 0.04499906817304014,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-07 00:00:00"),
            "fcst": -0.02204728075254085,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-08 00:00:00"),
            "fcst": 0.01887920114434169,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-09 00:00:00"),
            "fcst": 0.02807098731061397,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-10 00:00:00"),
            "fcst": -0.05101427096084735,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-11 00:00:00"),
            "fcst": -0.06567448275215854,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-12 00:00:00"),
            "fcst": -0.1661172146522294,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-13 00:00:00"),
            "fcst": -0.45662523636038066,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-14 00:00:00"),
            "fcst": -0.29484802919147635,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-07-15 00:00:00"),
            "fcst": -0.2576509951513812,
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        },
        {
            "time": pd.Timestamp("2013-02-25 00:00:00"),
            "fcst": 0.19628456887496087,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-02-26 00:00:00"),
            "fcst": 0.10019887553438628,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-02-27 00:00:00"),
            "fcst": -0.07520717053836065,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-02-28 00:00:00"),
            "fcst": 0.03298355039674053,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-01 00:00:00"),
            "fcst": 0.055433128301513895,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-02 00:00:00"),
            "fcst": 0.07429767219394756,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-03 00:00:00"),
            "fcst": 0.12722529339106078,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-04 00:00:00"),
            "fcst": 0.09775001275984066,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-05 00:00:00"),
            "fcst": 0.06088058445207728,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-06 00:00:00"),
            "fcst": 0.05298595317047888,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-07 00:00:00"),
            "fcst": 0.0436319001666092,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-08 00:00:00"),
            "fcst": 0.0036462114725477335,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-09 00:00:00"),
            "fcst": 0.08007940466841124,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-10 00:00:00"),
            "fcst": 0.15334923005918044,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-11 00:00:00"),
            "fcst": 0.17188681314212778,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-12 00:00:00"),
            "fcst": 0.0391287607636743,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-13 00:00:00"),
            "fcst": 0.013229061653719343,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-14 00:00:00"),
            "fcst": -0.11943900495684034,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-15 00:00:00"),
            "fcst": -0.251549296029257,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-16 00:00:00"),
            "fcst": -0.07707680814449921,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-17 00:00:00"),
            "fcst": 0.03050894015373598,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-18 00:00:00"),
            "fcst": 0.0027868934109876523,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-19 00:00:00"),
            "fcst": 0.030265271794574004,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-20 00:00:00"),
            "fcst": 0.005565862885094863,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-21 00:00:00"),
            "fcst": -0.15509033691841095,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-22 00:00:00"),
            "fcst": -0.29014076383492926,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-23 00:00:00"),
            "fcst": -0.014458080712184851,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-24 00:00:00"),
            "fcst": 0.07587250378642696,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-25 00:00:00"),
            "fcst": -0.21799719879296947,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-26 00:00:00"),
            "fcst": -0.03702319905105739,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-27 00:00:00"),
            "fcst": 0.07537751142657184,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-28 00:00:00"),
            "fcst": 0.10904266652649494,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-29 00:00:00"),
            "fcst": 0.1005320948493634,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-30 00:00:00"),
            "fcst": 0.09066200227821583,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-03-31 00:00:00"),
            "fcst": 0.08356274096954996,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-01 00:00:00"),
            "fcst": 0.21137564308564738,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-02 00:00:00"),
            "fcst": 0.06544169132038052,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-03 00:00:00"),
            "fcst": 0.12859701218060596,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-04 00:00:00"),
            "fcst": 0.07555411988986832,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-05 00:00:00"),
            "fcst": 0.1352649420476866,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-06 00:00:00"),
            "fcst": 0.16360959089774735,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-07 00:00:00"),
            "fcst": 0.1504991416635415,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-08 00:00:00"),
            "fcst": 0.08522631678613442,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-09 00:00:00"),
            "fcst": 0.03271626929061449,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-10 00:00:00"),
            "fcst": 0.012032987561415326,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-11 00:00:00"),
            "fcst": 0.06226741257140378,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-12 00:00:00"),
            "fcst": -0.18634053078122764,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-13 00:00:00"),
            "fcst": 0.00991478058534373,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-14 00:00:00"),
            "fcst": 0.16666254287310428,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-15 00:00:00"),
            "fcst": 0.15082718804260112,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-16 00:00:00"),
            "fcst": 0.07854208836446688,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-17 00:00:00"),
            "fcst": 0.02887891023785722,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-18 00:00:00"),
            "fcst": 0.028061008419253963,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-19 00:00:00"),
            "fcst": -0.008738806323105697,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-20 00:00:00"),
            "fcst": -0.10980505360053733,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-21 00:00:00"),
            "fcst": 0.15770171409854744,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-22 00:00:00"),
            "fcst": 0.035181913531413034,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-23 00:00:00"),
            "fcst": -0.038126964262058166,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-24 00:00:00"),
            "fcst": -0.05833577687965125,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-25 00:00:00"),
            "fcst": -0.18615676011966428,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-26 00:00:00"),
            "fcst": -0.15335795649936085,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-27 00:00:00"),
            "fcst": -0.22953567971841624,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-28 00:00:00"),
            "fcst": -0.11566596410555033,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-29 00:00:00"),
            "fcst": -0.017777493672709754,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-04-30 00:00:00"),
            "fcst": -0.25853291624371877,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-05-01 00:00:00"),
            "fcst": -0.11594591029261868,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2013-05-01 00:00:00"),
            "fcst": 7.940480857230361,
            "fcst_lower": 7.05954539696271,
            "fcst_upper": 8.821416317498013,
        },
        {
            "time": pd.Timestamp("2013-05-02 00:00:00"),
            "fcst": 7.890521247867683,
            "fcst_lower": 6.895622219348986,
            "fcst_upper": 8.88542027638638,
        },
        {
            "time": pd.Timestamp("2013-05-03 00:00:00"),
            "fcst": 7.868743227314358,
            "fcst_lower": 6.834214576608463,
            "fcst_upper": 8.903271878020252,
        },
        {
            "time": pd.Timestamp("2013-05-04 00:00:00"),
            "fcst": 7.859001067790166,
            "fcst_lower": 6.803917944580299,
            "fcst_upper": 8.914084191000033,
        },
        {
            "time": pd.Timestamp("2013-05-05 00:00:00"),
            "fcst": 7.854399212870253,
            "fcst_lower": 6.784960481789183,
            "fcst_upper": 8.923837943951323,
        },
        {
            "time": pd.Timestamp("2013-05-06 00:00:00"),
            "fcst": 7.851992691658566,
            "fcst_lower": 6.770545536610528,
            "fcst_upper": 8.933439846706605,
        },
        {
            "time": pd.Timestamp("2013-05-07 00:00:00"),
            "fcst": 7.850523758870425,
            "fcst_lower": 6.758058474764527,
            "fcst_upper": 8.942989042976322,
        },
        {
            "time": pd.Timestamp("2013-05-08 00:00:00"),
            "fcst": 7.849455253611216,
            "fcst_lower": 6.746435277314804,
            "fcst_upper": 8.952475229907629,
        },
        {
            "time": pd.Timestamp("2013-05-09 00:00:00"),
            "fcst": 7.848557763908408,
            "fcst_lower": 6.7352309284064615,
            "fcst_upper": 8.961884599410356,
        },
        {
            "time": pd.Timestamp("2013-05-10 00:00:00"),
            "fcst": 7.847733311942562,
            "fcst_lower": 6.7242563949242085,
            "fcst_upper": 8.971210228960917,
        },
        {
            "time": pd.Timestamp("2013-05-11 00:00:00"),
            "fcst": 7.846940053109861,
            "fcst_lower": 6.71343022358396,
            "fcst_upper": 8.980449882635762,
        },
        {
            "time": pd.Timestamp("2013-05-12 00:00:00"),
            "fcst": 7.84616011631582,
            "fcst_lower": 6.702716519862747,
            "fcst_upper": 8.989603712768893,
        },
        {
            "time": pd.Timestamp("2013-05-13 00:00:00"),
            "fcst": 7.845385869130233,
            "fcst_lower": 6.692098772048167,
            "fcst_upper": 8.998672966212299,
        },
        {
            "time": pd.Timestamp("2013-05-14 00:00:00"),
            "fcst": 7.844614051876368,
            "fcst_lower": 6.681568754691981,
            "fcst_upper": 9.007659349060756,
        },
        {
            "time": pd.Timestamp("2013-05-15 00:00:00"),
            "fcst": 7.843843272403614,
            "fcst_lower": 6.671121814034725,
            "fcst_upper": 9.016564730772503,
        },
    ]
)

PEYTON_FCST_30_ARIMA_PARAM_1_MODEL_1_INCL_HIST = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("2012-05-03 00:00:00"),
            "fcst": -0.0007700058570090651,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-04 00:00:00"),
            "fcst": 0.003945764176497557,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-05 00:00:00"),
            "fcst": 0.056389253940629255,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-06 00:00:00"),
            "fcst": 0.03581633435595588,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-07 00:00:00"),
            "fcst": -0.4128869117836815,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-08 00:00:00"),
            "fcst": -0.21494697202566299,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-09 00:00:00"),
            "fcst": 0.023753499987732296,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-10 00:00:00"),
            "fcst": 0.09355937802330405,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-11 00:00:00"),
            "fcst": 0.18319088827293034,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-12 00:00:00"),
            "fcst": 0.20044808320056756,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-13 00:00:00"),
            "fcst": 0.2503354760854294,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-14 00:00:00"),
            "fcst": 0.17127042819947244,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-15 00:00:00"),
            "fcst": 0.14608737892963222,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-16 00:00:00"),
            "fcst": 0.1046934406435516,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-17 00:00:00"),
            "fcst": 0.022584545049575466,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-18 00:00:00"),
            "fcst": 0.05981881271357159,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-19 00:00:00"),
            "fcst": 0.14025155875910666,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-20 00:00:00"),
            "fcst": 0.2620310138400047,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-21 00:00:00"),
            "fcst": 0.20643571244428782,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-22 00:00:00"),
            "fcst": -0.03163734777951549,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-23 00:00:00"),
            "fcst": -0.17638166890869264,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-24 00:00:00"),
            "fcst": -0.026283311915276913,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-25 00:00:00"),
            "fcst": 0.08888312964866367,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-26 00:00:00"),
            "fcst": 0.09318350600838077,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-27 00:00:00"),
            "fcst": 0.21884338795240824,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-28 00:00:00"),
            "fcst": 0.11943606160735805,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-29 00:00:00"),
            "fcst": 0.09954952209841111,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-30 00:00:00"),
            "fcst": 0.0115899772154675,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("2012-05-31 00:00:00"),
            "fcst": 0.012880997922767479,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
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            "time": pd.Timestamp("2013-05-03 00:00:00"),
            "fcst": 7.868743227314358,
            "fcst_lower": 6.834214576608463,
            "fcst_upper": 8.903271878020252,
        },
        {
            "time": pd.Timestamp("2013-05-04 00:00:00"),
            "fcst": 7.859001067790166,
            "fcst_lower": 6.803917944580299,
            "fcst_upper": 8.914084191000033,
        },
        {
            "time": pd.Timestamp("2013-05-05 00:00:00"),
            "fcst": 7.854399212870253,
            "fcst_lower": 6.784960481789183,
            "fcst_upper": 8.923837943951323,
        },
        {
            "time": pd.Timestamp("2013-05-06 00:00:00"),
            "fcst": 7.851992691658566,
            "fcst_lower": 6.770545536610528,
            "fcst_upper": 8.933439846706605,
        },
        {
            "time": pd.Timestamp("2013-05-07 00:00:00"),
            "fcst": 7.850523758870425,
            "fcst_lower": 6.758058474764527,
            "fcst_upper": 8.942989042976322,
        },
        {
            "time": pd.Timestamp("2013-05-08 00:00:00"),
            "fcst": 7.849455253611216,
            "fcst_lower": 6.746435277314804,
            "fcst_upper": 8.952475229907629,
        },
        {
            "time": pd.Timestamp("2013-05-09 00:00:00"),
            "fcst": 7.848557763908408,
            "fcst_lower": 6.7352309284064615,
            "fcst_upper": 8.961884599410356,
        },
        {
            "time": pd.Timestamp("2013-05-10 00:00:00"),
            "fcst": 7.847733311942562,
            "fcst_lower": 6.7242563949242085,
            "fcst_upper": 8.971210228960917,
        },
        {
            "time": pd.Timestamp("2013-05-11 00:00:00"),
            "fcst": 7.846940053109861,
            "fcst_lower": 6.71343022358396,
            "fcst_upper": 8.980449882635762,
        },
        {
            "time": pd.Timestamp("2013-05-12 00:00:00"),
            "fcst": 7.84616011631582,
            "fcst_lower": 6.702716519862747,
            "fcst_upper": 8.989603712768893,
        },
        {
            "time": pd.Timestamp("2013-05-13 00:00:00"),
            "fcst": 7.845385869130233,
            "fcst_lower": 6.692098772048167,
            "fcst_upper": 8.998672966212299,
        },
        {
            "time": pd.Timestamp("2013-05-14 00:00:00"),
            "fcst": 7.844614051876368,
            "fcst_lower": 6.681568754691981,
            "fcst_upper": 9.007659349060756,
        },
        {
            "time": pd.Timestamp("2013-05-15 00:00:00"),
            "fcst": 7.843843272403614,
            "fcst_lower": 6.671121814034725,
            "fcst_upper": 9.016564730772503,
        },
        {
            "time": pd.Timestamp("2013-05-16 00:00:00"),
            "fcst": 7.843072936148924,
            "fcst_lower": 6.660754861687368,
            "fcst_upper": 9.02539101061048,
        },
        {
            "time": pd.Timestamp("2013-05-17 00:00:00"),
            "fcst": 7.842302789184875,
            "fcst_lower": 6.65046551906174,
            "fcst_upper": 9.03414005930801,
        },
        {
            "time": pd.Timestamp("2013-05-18 00:00:00"),
            "fcst": 7.841532723063531,
            "fcst_lower": 6.6402517511320145,
            "fcst_upper": 9.042813694995047,
        },
        {
            "time": pd.Timestamp("2013-05-19 00:00:00"),
            "fcst": 7.840762691468684,
            "fcst_lower": 6.630111708424872,
            "fcst_upper": 9.051413674512496,
        },
        {
            "time": pd.Timestamp("2013-05-20 00:00:00"),
            "fcst": 7.839992674619497,
            "fcst_lower": 6.620043657716803,
            "fcst_upper": 9.05994169152219,
        },
        {
            "time": pd.Timestamp("2013-05-21 00:00:00"),
            "fcst": 7.839222664067922,
            "fcst_lower": 6.610045950601637,
            "fcst_upper": 9.068399377534208,
        },
        {
            "time": pd.Timestamp("2013-05-22 00:00:00"),
            "fcst": 7.838452656205947,
            "fcst_lower": 6.600117008296207,
            "fcst_upper": 9.076788304115686,
        },
        {
            "time": pd.Timestamp("2013-05-23 00:00:00"),
            "fcst": 7.837682649492652,
            "fcst_lower": 6.590255313472834,
            "fcst_upper": 9.085109985512469,
        },
        {
            "time": pd.Timestamp("2013-05-24 00:00:00"),
            "fcst": 7.836912643269938,
            "fcst_lower": 6.580459405190895,
            "fcst_upper": 9.09336588134898,
        },
        {
            "time": pd.Timestamp("2013-05-25 00:00:00"),
            "fcst": 7.836142637256742,
            "fcst_lower": 6.570727875248046,
            "fcst_upper": 9.101557399265438,
        },
        {
            "time": pd.Timestamp("2013-05-26 00:00:00"),
            "fcst": 7.835372631333029,
            "fcst_lower": 6.561059365228859,
            "fcst_upper": 9.109685897437199,
        },
        {
            "time": pd.Timestamp("2013-05-27 00:00:00"),
            "fcst": 7.834602625447531,
            "fcst_lower": 6.5514525639365075,
            "fcst_upper": 9.117752686958555,
        },
        {
            "time": pd.Timestamp("2013-05-28 00:00:00"),
            "fcst": 7.833832619578356,
            "fcst_lower": 6.541906205067343,
            "fcst_upper": 9.125759034089368,
        },
        {
            "time": pd.Timestamp("2013-05-29 00:00:00"),
            "fcst": 7.833062613716151,
            "fcst_lower": 6.532419065062822,
            "fcst_upper": 9.13370616236948,
        },
        {
            "time": pd.Timestamp("2013-05-30 00:00:00"),
            "fcst": 7.832292607856923,
            "fcst_lower": 6.522989961105574,
            "fcst_upper": 9.141595254608273,
        },
    ]
)

AIR_FCST_15_SARIMA_PARAM_1_MODEL_1 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 475.734999664335,
            "fcst_lower": 414.94683614194525,
            "fcst_upper": 536.5231631867248,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 454.9960131326432,
            "fcst_lower": 350.9406985933037,
            "fcst_upper": 559.0513276719827,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 464.83036926989587,
            "fcst_lower": 337.5891858591327,
            "fcst_upper": 592.071552680659,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 460.16695138848075,
            "fcst_lower": 310.6316031682349,
            "fcst_upper": 609.7022996087267,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 462.3783281205398,
            "fcst_lower": 294.62476708969064,
            "fcst_upper": 630.1318891513889,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 461.3297008742918,
            "fcst_lower": 276.654109966928,
            "fcst_upper": 646.0052917816556,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 461.82695631052394,
            "fcst_lower": 261.87302294007793,
            "fcst_upper": 661.78088968097,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 461.591159491638,
            "fcst_lower": 247.3497039224739,
            "fcst_upper": 675.832615060802,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 461.7029735327635,
            "fcst_lower": 234.11208345911712,
            "fcst_upper": 689.2938636064099,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 461.64995169874607,
            "fcst_lower": 221.43104743037904,
            "fcst_upper": 701.8688559671131,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 461.67509447021087,
            "fcst_lower": 209.4683909799995,
            "fcst_upper": 713.8817979604222,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 461.66317185439334,
            "fcst_lower": 198.00921052859343,
            "fcst_upper": 725.3171331801932,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 461.6688255178382,
            "fcst_lower": 187.04615729497976,
            "fcst_upper": 736.2914937406965,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 461.66614457006324,
            "fcst_lower": 176.49553139848467,
            "fcst_upper": 746.8367577416418,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 461.6674158662842,
            "fcst_lower": 166.32570802200962,
            "fcst_upper": 757.0091237105588,
        },
    ]
)

AIR_FCST_30_SARIMA_PARAM_1_MODEL_1 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 475.734999664335,
            "fcst_lower": 414.94683614194525,
            "fcst_upper": 536.5231631867248,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 454.9960131326432,
            "fcst_lower": 350.9406985933037,
            "fcst_upper": 559.0513276719827,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 464.83036926989587,
            "fcst_lower": 337.5891858591327,
            "fcst_upper": 592.071552680659,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 460.16695138848075,
            "fcst_lower": 310.6316031682349,
            "fcst_upper": 609.7022996087267,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 462.3783281205398,
            "fcst_lower": 294.62476708969064,
            "fcst_upper": 630.1318891513889,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 461.3297008742918,
            "fcst_lower": 276.654109966928,
            "fcst_upper": 646.0052917816556,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 461.82695631052394,
            "fcst_lower": 261.87302294007793,
            "fcst_upper": 661.78088968097,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 461.591159491638,
            "fcst_lower": 247.3497039224739,
            "fcst_upper": 675.832615060802,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 461.7029735327635,
            "fcst_lower": 234.11208345911712,
            "fcst_upper": 689.2938636064099,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 461.64995169874607,
            "fcst_lower": 221.43104743037904,
            "fcst_upper": 701.8688559671131,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 461.67509447021087,
            "fcst_lower": 209.4683909799995,
            "fcst_upper": 713.8817979604222,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 461.66317185439334,
            "fcst_lower": 198.00921052859343,
            "fcst_upper": 725.3171331801932,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 461.6688255178382,
            "fcst_lower": 187.04615729497976,
            "fcst_upper": 736.2914937406965,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 461.66614457006324,
            "fcst_lower": 176.49553139848467,
            "fcst_upper": 746.8367577416418,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 461.6674158662842,
            "fcst_lower": 166.32570802200962,
            "fcst_upper": 757.0091237105588,
        },
        {
            "time": pd.Timestamp("1962-04-01 00:00:00"),
            "fcst": 461.66681302198543,
            "fcst_lower": 156.49263999202753,
            "fcst_upper": 766.8409860519433,
        },
        {
            "time": pd.Timestamp("1962-05-01 00:00:00"),
            "fcst": 461.66709888868,
            "fcst_lower": 146.96759579695265,
            "fcst_upper": 776.3666019804074,
        },
        {
            "time": pd.Timestamp("1962-06-01 00:00:00"),
            "fcst": 461.6669633316759,
            "fcst_lower": 137.72205693628518,
            "fcst_upper": 785.6118697270666,
        },
        {
            "time": pd.Timestamp("1962-07-01 00:00:00"),
            "fcst": 461.66702761234137,
            "fcst_lower": 128.7333762035309,
            "fcst_upper": 794.6006790211518,
        },
        {
            "time": pd.Timestamp("1962-08-01 00:00:00"),
            "fcst": 461.6669971306707,
            "fcst_lower": 119.98097832591253,
            "fcst_upper": 803.3530159354289,
        },
        {
            "time": pd.Timestamp("1962-09-01 00:00:00"),
            "fcst": 461.6670115849742,
            "fcst_lower": 111.44729181160483,
            "fcst_upper": 811.8867313583436,
        },
        {
            "time": pd.Timestamp("1962-10-01 00:00:00"),
            "fcst": 461.66700473079317,
            "fcst_lower": 103.11663171244152,
            "fcst_upper": 820.2173777491448,
        },
        {
            "time": pd.Timestamp("1962-11-01 00:00:00"),
            "fcst": 461.66700798102227,
            "fcst_lower": 94.9751928349915,
            "fcst_upper": 828.358823127053,
        },
        {
            "time": pd.Timestamp("1962-12-01 00:00:00"),
            "fcst": 461.6670064397748,
            "fcst_lower": 87.01062404537282,
            "fcst_upper": 836.3233888341767,
        },
        {
            "time": pd.Timestamp("1963-01-01 00:00:00"),
            "fcst": 461.66700717062906,
            "fcst_lower": 79.21188259460115,
            "fcst_upper": 844.122131746657,
        },
        {
            "time": pd.Timestamp("1963-02-01 00:00:00"),
            "fcst": 461.6670068240605,
            "fcst_lower": 71.56901936341609,
            "fcst_upper": 851.7649942847049,
        },
        {
            "time": pd.Timestamp("1963-03-01 00:00:00"),
            "fcst": 461.6670069884021,
            "fcst_lower": 64.07304664895531,
            "fcst_upper": 859.2609673278489,
        },
        {
            "time": pd.Timestamp("1963-04-01 00:00:00"),
            "fcst": 461.66700691047186,
            "fcst_lower": 56.715806406470165,
            "fcst_upper": 866.6182074144735,
        },
        {
            "time": pd.Timestamp("1963-05-01 00:00:00"),
            "fcst": 461.6670069474261,
            "fcst_lower": 49.48986993667626,
            "fcst_upper": 873.8441439581759,
        },
        {
            "time": pd.Timestamp("1963-06-01 00:00:00"),
            "fcst": 461.6670069299025,
            "fcst_lower": 42.388448257749985,
            "fcst_upper": 880.945565602055,
        },
    ]
)

AIR_FCST_15_SARIMA_PARAM_2_MODEL_1 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 449.45646723526534,
            "fcst_lower": 428.8051912510871,
            "fcst_upper": 470.1077432194436,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 419.4161652450919,
            "fcst_lower": 395.57372723856895,
            "fcst_upper": 443.2586032516149,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 467.96439603968764,
            "fcst_lower": 440.41536929301304,
            "fcst_upper": 495.51342278636224,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 489.5373489111334,
            "fcst_lower": 458.2564055096148,
            "fcst_upper": 520.8182923126519,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 505.5361446094845,
            "fcst_lower": 471.5949596047826,
            "fcst_upper": 539.4773296141864,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 578.1914387473729,
            "fcst_lower": 541.2893697134359,
            "fcst_upper": 615.0935077813099,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 670.4043449527954,
            "fcst_lower": 631.0355334792789,
            "fcst_upper": 709.773156426312,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 663.0898849729405,
            "fcst_lower": 621.2895232734636,
            "fcst_upper": 704.8902466724173,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 547.4938044426624,
            "fcst_lower": 503.4205663546901,
            "fcst_upper": 591.5670425306347,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 486.3639955812308,
            "fcst_lower": 440.13923220066494,
            "fcst_upper": 532.5887589617968,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 412.8461658627194,
            "fcst_lower": 364.54878660265763,
            "fcst_upper": 461.1435451227811,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 459.8220185004234,
            "fcst_lower": 409.550641470057,
            "fcst_upper": 510.0933955307898,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 477.5535520899389,
            "fcst_lower": 419.98487039591237,
            "fcst_upper": 535.1222337839654,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 443.810928145418,
            "fcst_lower": 382.15096882917675,
            "fcst_upper": 505.4708874616593,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 496.9279986362585,
            "fcst_lower": 430.9861627523478,
            "fcst_upper": 562.8698345201692,
        },
    ]
)

AIR_FCST_30_SARIMA_PARAM_2_MODEL_1 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 449.45646723526534,
            "fcst_lower": 428.8051912510871,
            "fcst_upper": 470.1077432194436,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 419.4161652450919,
            "fcst_lower": 395.57372723856895,
            "fcst_upper": 443.2586032516149,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 467.96439603968764,
            "fcst_lower": 440.41536929301304,
            "fcst_upper": 495.51342278636224,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 489.5373489111334,
            "fcst_lower": 458.2564055096148,
            "fcst_upper": 520.8182923126519,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 505.5361446094845,
            "fcst_lower": 471.5949596047826,
            "fcst_upper": 539.4773296141864,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 578.1914387473729,
            "fcst_lower": 541.2893697134359,
            "fcst_upper": 615.0935077813099,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 670.4043449527954,
            "fcst_lower": 631.0355334792789,
            "fcst_upper": 709.773156426312,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 663.0898849729405,
            "fcst_lower": 621.2895232734636,
            "fcst_upper": 704.8902466724173,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 547.4938044426624,
            "fcst_lower": 503.4205663546901,
            "fcst_upper": 591.5670425306347,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 486.3639955812308,
            "fcst_lower": 440.13923220066494,
            "fcst_upper": 532.5887589617968,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 412.8461658627194,
            "fcst_lower": 364.54878660265763,
            "fcst_upper": 461.1435451227811,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 459.8220185004234,
            "fcst_lower": 409.550641470057,
            "fcst_upper": 510.0933955307898,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 477.5535520899389,
            "fcst_lower": 419.98487039591237,
            "fcst_upper": 535.1222337839654,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 443.810928145418,
            "fcst_lower": 382.15096882917675,
            "fcst_upper": 505.4708874616593,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 496.9279986362585,
            "fcst_lower": 430.9861627523478,
            "fcst_upper": 562.8698345201692,
        },
        {
            "time": pd.Timestamp("1962-04-01 00:00:00"),
            "fcst": 520.403839900848,
            "fcst_lower": 450.2070928538659,
            "fcst_upper": 590.60058694783,
        },
        {
            "time": pd.Timestamp("1962-05-01 00:00:00"),
            "fcst": 537.4342519864693,
            "fcst_lower": 463.58280669416007,
            "fcst_upper": 611.2856972787787,
        },
        {
            "time": pd.Timestamp("1962-06-01 00:00:00"),
            "fcst": 617.914355864871,
            "fcst_lower": 540.3104378414018,
            "fcst_upper": 695.5182738883402,
        },
        {
            "time": pd.Timestamp("1962-07-01 00:00:00"),
            "fcst": 720.3515864522939,
            "fcst_lower": 639.3206536889036,
            "fcst_upper": 801.3825192156842,
        },
        {
            "time": pd.Timestamp("1962-08-01 00:00:00"),
            "fcst": 711.254748925014,
            "fcst_lower": 626.872931320103,
            "fcst_upper": 795.6365665299251,
        },
        {
            "time": pd.Timestamp("1962-09-01 00:00:00"),
            "fcst": 580.924029262118,
            "fcst_lower": 493.33371987320817,
            "fcst_upper": 668.5143386510279,
        },
        {
            "time": pd.Timestamp("1962-10-01 00:00:00"),
            "fcst": 511.563934970003,
            "fcst_lower": 420.88428949185214,
            "fcst_upper": 602.2435804481538,
        },
        {
            "time": pd.Timestamp("1962-11-01 00:00:00"),
            "fcst": 428.33070727185594,
            "fcst_lower": 334.65357514032905,
            "fcst_upper": 522.0078394033828,
        },
        {
            "time": pd.Timestamp("1962-12-01 00:00:00"),
            "fcst": 480.03619512275634,
            "fcst_lower": 383.46248741000784,
            "fcst_upper": 576.6099028355048,
        },
        {
            "time": pd.Timestamp("1963-01-01 00:00:00"),
            "fcst": 498.98075596714943,
            "fcst_lower": 394.73275010117237,
            "fcst_upper": 603.2287618331264,
        },
        {
            "time": pd.Timestamp("1963-02-01 00:00:00"),
            "fcst": 460.27256842992364,
            "fcst_lower": 351.05176802520236,
            "fcst_upper": 569.4933688346449,
        },
        {
            "time": pd.Timestamp("1963-03-01 00:00:00"),
            "fcst": 518.833847610495,
            "fcst_lower": 404.43654763263567,
            "fcst_upper": 633.2311475883544,
        },
        {
            "time": pd.Timestamp("1963-04-01 00:00:00"),
            "fcst": 544.1925791044939,
            "fcst_lower": 424.6196188978465,
            "fcst_upper": 663.7655393111413,
        },
        {
            "time": pd.Timestamp("1963-05-01 00:00:00"),
            "fcst": 562.3256287957417,
            "fcst_lower": 438.1373152385748,
            "fcst_upper": 686.5139423529087,
        },
        {
            "time": pd.Timestamp("1963-06-01 00:00:00"),
            "fcst": 651.5118577810393,
            "fcst_lower": 522.6088426209153,
            "fcst_upper": 780.4148729411634,
        },
    ]
)

AIR_FCST_15_SARIMA_PARAM_1_MODEL_2 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 792.0535887542746,
            "fcst_lower": -3338.966274757853,
            "fcst_upper": 4923.0734522664025,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 1152.0732316134322,
            "fcst_lower": -11883.898354356968,
            "fcst_upper": 14188.044817583834,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 1512.0589317778959,
            "fcst_lower": -22863.1074551212,
            "fcst_upper": 25887.225318676996,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 1872.0106924477868,
            "fcst_lower": -35879.791633738336,
            "fcst_upper": 39623.81301863391,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 2231.9285168229244,
            "fcst_lower": -50669.34240608488,
            "fcst_upper": 55133.19943973073,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 2591.812408102827,
            "fcst_lower": -67047.30346095053,
            "fcst_upper": 72230.92827715617,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 2951.6623694867103,
            "fcst_lower": -84876.3873239775,
            "fcst_upper": 90779.71206295093,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 3311.4784041734893,
            "fcst_lower": -104049.44315289322,
            "fcst_upper": 110672.39996124021,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 3671.260515361777,
            "fcst_lower": -124479.88114782744,
            "fcst_upper": 131822.402178551,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 4031.0087062498847,
            "fcst_lower": -146095.857910656,
            "fcst_upper": 154157.87532315575,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 4390.722980035823,
            "fcst_lower": -168836.52367518313,
            "fcst_upper": 177617.96963525476,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 4750.4033399172995,
            "fcst_lower": -192649.4805148904,
            "fcst_upper": 202150.287194725,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 5110.049789091721,
            "fcst_lower": -217488.99250936016,
            "fcst_upper": 227709.0920875436,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 5469.662330756194,
            "fcst_lower": -243314.6845571667,
            "fcst_upper": 254254.0092186791,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 5829.240968107521,
            "fcst_lower": -270090.57091421064,
            "fcst_upper": 281749.0528504257,
        },
    ]
)

AIR_FCST_30_SARIMA_PARAM_1_MODEL_2 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 792.0535887542746,
            "fcst_lower": -3338.966274757853,
            "fcst_upper": 4923.0734522664025,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 1152.0732316134322,
            "fcst_lower": -11883.898354356968,
            "fcst_upper": 14188.044817583834,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 1512.0589317778959,
            "fcst_lower": -22863.1074551212,
            "fcst_upper": 25887.225318676996,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 1872.0106924477868,
            "fcst_lower": -35879.791633738336,
            "fcst_upper": 39623.81301863391,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 2231.9285168229244,
            "fcst_lower": -50669.34240608488,
            "fcst_upper": 55133.19943973073,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 2591.812408102827,
            "fcst_lower": -67047.30346095053,
            "fcst_upper": 72230.92827715617,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 2951.6623694867103,
            "fcst_lower": -84876.3873239775,
            "fcst_upper": 90779.71206295093,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 3311.4784041734893,
            "fcst_lower": -104049.44315289322,
            "fcst_upper": 110672.39996124021,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 3671.260515361777,
            "fcst_lower": -124479.88114782744,
            "fcst_upper": 131822.402178551,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 4031.0087062498847,
            "fcst_lower": -146095.857910656,
            "fcst_upper": 154157.87532315575,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 4390.722980035823,
            "fcst_lower": -168836.52367518313,
            "fcst_upper": 177617.96963525476,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 4750.4033399172995,
            "fcst_lower": -192649.4805148904,
            "fcst_upper": 202150.287194725,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 5110.049789091721,
            "fcst_lower": -217488.99250936016,
            "fcst_upper": 227709.0920875436,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 5469.662330756194,
            "fcst_lower": -243314.6845571667,
            "fcst_upper": 254254.0092186791,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 5829.240968107521,
            "fcst_lower": -270090.57091421064,
            "fcst_upper": 281749.0528504257,
        },
        {
            "time": pd.Timestamp("1962-04-01 00:00:00"),
            "fcst": 6188.785704342207,
            "fcst_lower": -297784.3133783731,
            "fcst_upper": 310161.88478705747,
        },
        {
            "time": pd.Timestamp("1962-05-01 00:00:00"),
            "fcst": 6548.2965426564515,
            "fcst_lower": -326366.6437887754,
            "fcst_upper": 339463.23687408824,
        },
        {
            "time": pd.Timestamp("1962-06-01 00:00:00"),
            "fcst": 6907.773486246155,
            "fcst_lower": -355810.906857057,
            "fcst_upper": 369626.45382954937,
        },
        {
            "time": pd.Timestamp("1962-07-01 00:00:00"),
            "fcst": 7267.216538306916,
            "fcst_lower": -386092.6929207302,
            "fcst_upper": 400627.12599734403,
        },
        {
            "time": pd.Timestamp("1962-08-01 00:00:00"),
            "fcst": 7626.625702034032,
            "fcst_lower": -417189.53909736965,
            "fcst_upper": 432442.79050143773,
        },
        {
            "time": pd.Timestamp("1962-09-01 00:00:00"),
            "fcst": 7986.000980622499,
            "fcst_lower": -449080.6832931032,
            "fcst_upper": 465052.68525434815,
        },
        {
            "time": pd.Timestamp("1962-10-01 00:00:00"),
            "fcst": 8345.342377267012,
            "fcst_lower": -481746.8596283498,
            "fcst_upper": 498437.5443828838,
        },
        {
            "time": pd.Timestamp("1962-11-01 00:00:00"),
            "fcst": 8704.649895161965,
            "fcst_lower": -515170.1267288102,
            "fcst_upper": 532579.4265191341,
        },
        {
            "time": pd.Timestamp("1962-12-01 00:00:00"),
            "fcst": 9063.92353750145,
            "fcst_lower": -549333.7223926808,
            "fcst_upper": 567461.5694676838,
        },
        {
            "time": pd.Timestamp("1963-01-01 00:00:00"),
            "fcst": 9423.163307479259,
            "fcst_lower": -584221.9396447878,
            "fcst_upper": 603068.2662597463,
        },
        {
            "time": pd.Timestamp("1963-02-01 00:00:00"),
            "fcst": 9782.369208288881,
            "fcst_lower": -619820.0202950516,
            "fcst_upper": 639384.7587116293,
        },
        {
            "time": pd.Timestamp("1963-03-01 00:00:00"),
            "fcst": 10141.541243123507,
            "fcst_lower": -656114.062946607,
            "fcst_upper": 676397.145432854,
        },
        {
            "time": pd.Timestamp("1963-04-01 00:00:00"),
            "fcst": 10500.679415176024,
            "fcst_lower": -693090.9430259921,
            "fcst_upper": 714092.3018563442,
        },
        {
            "time": pd.Timestamp("1963-05-01 00:00:00"),
            "fcst": 10859.783727639018,
            "fcst_lower": -730738.2428882846,
            "fcst_upper": 752457.8103435626,
        },
        {
            "time": pd.Timestamp("1963-06-01 00:00:00"),
            "fcst": 11218.854183704778,
            "fcst_lower": -769044.1904220572,
            "fcst_upper": 791481.8987894668,
        },
    ]
)

AIR_FCST_15_SARIMA_PARAM_2_MODEL_2 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": -3781.8669719150857,
            "fcst_lower": -200181.97843355636,
            "fcst_upper": 192618.24448972617,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 457362.75075375783,
            "fcst_lower": -20739325.635134727,
            "fcst_upper": 21654051.13664224,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": -50142237.42101226,
            "fcst_lower": -2376266233.751603,
            "fcst_upper": 2275981758.9095783,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 5501568363.932395,
            "fcst_lower": -249716603522.0544,
            "fcst_upper": 260719740249.91922,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": -603623464481.4572,
            "fcst_lower": -28605764152574.383,
            "fcst_upper": 27398517223611.47,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 66228625935946.9,
            "fcst_lower": -3006122613812477.0,
            "fcst_upper": 3138579865684371.0,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": -7266501633095522.0,
            "fcst_lower": -3.443600919917305e17,
            "fcst_upper": 3.298270887255394e17,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 7.972692357411579e17,
            "fcst_lower": -3.61881142030236e19,
            "fcst_upper": 3.778265267450592e19,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": -8.747513815509372e19,
            "fcst_lower": -4.145453774465402e21,
            "fcst_upper": 3.970503498155214e21,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 9.59763584523536e21,
            "fcst_lower": -4.356374565532236e23,
            "fcst_upper": 4.548327282436943e23,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": -1.0530376488737538e24,
            "fcst_lower": -4.9903538173762575e25,
            "fcst_upper": 4.779746287601506e25,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 1.1553764987823104e26,
            "fcst_lower": -5.244263143623692e27,
            "fcst_upper": 5.475338443380155e27,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": -1.2676610901482663e28,
            "fcst_lower": -6.007456017481067e29,
            "fcst_upper": 5.7539237994514136e29,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 1.3908579940560712e30,
            "fcst_lower": -6.313115528946667e31,
            "fcst_upper": 6.591287127757882e31,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": -1.5260277172374359e32,
            "fcst_lower": -7.231857524071115e33,
            "fcst_upper": 6.926651980623627e33,
        },
    ]
)

AIR_FCST_30_SARIMA_PARAM_2_MODEL_2 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": -3781.8669719150857,
            "fcst_lower": -200181.97843355636,
            "fcst_upper": 192618.24448972617,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 457362.75075375783,
            "fcst_lower": -20739325.635134727,
            "fcst_upper": 21654051.13664224,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": -50142237.42101226,
            "fcst_lower": -2376266233.751603,
            "fcst_upper": 2275981758.9095783,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 5501568363.932395,
            "fcst_lower": -249716603522.0544,
            "fcst_upper": 260719740249.91922,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": -603623464481.4572,
            "fcst_lower": -28605764152574.383,
            "fcst_upper": 27398517223611.47,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 66228625935946.9,
            "fcst_lower": -3006122613812477.0,
            "fcst_upper": 3138579865684371.0,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": -7266501633095522.0,
            "fcst_lower": -3.443600919917305e17,
            "fcst_upper": 3.298270887255394e17,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 7.972692357411579e17,
            "fcst_lower": -3.61881142030236e19,
            "fcst_upper": 3.778265267450592e19,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": -8.747513815509372e19,
            "fcst_lower": -4.145453774465402e21,
            "fcst_upper": 3.970503498155214e21,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 9.59763584523536e21,
            "fcst_lower": -4.356374565532236e23,
            "fcst_upper": 4.548327282436943e23,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": -1.0530376488737538e24,
            "fcst_lower": -4.9903538173762575e25,
            "fcst_upper": 4.779746287601506e25,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 1.1553764987823104e26,
            "fcst_lower": -5.244263143623692e27,
            "fcst_upper": 5.475338443380155e27,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": -1.2676610901482663e28,
            "fcst_lower": -6.007456017481067e29,
            "fcst_upper": 5.7539237994514136e29,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 1.3908579940560712e30,
            "fcst_lower": -6.313115528946667e31,
            "fcst_upper": 6.591287127757882e31,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": -1.5260277172374359e32,
            "fcst_lower": -7.231857524071115e33,
            "fcst_upper": 6.926651980623627e33,
        },
        {
            "time": pd.Timestamp("1962-04-01 00:00:00"),
            "fcst": 1.6743338311524398e34,
            "fcst_lower": -7.59981461462061e35,
            "fcst_upper": 7.934681380851099e35,
        },
        {
            "time": pd.Timestamp("1962-05-01 00:00:00"),
            "fcst": -1.8370529882750648e36,
            "fcst_lower": -8.705808764355019e37,
            "fcst_upper": 8.338398166700007e37,
        },
        {
            "time": pd.Timestamp("1962-06-01 00:00:00"),
            "fcst": 2.0155859118055944e38,
            "fcst_lower": -9.148760530640518e39,
            "fcst_upper": 9.551877713001636e39,
        },
        {
            "time": pd.Timestamp("1962-07-01 00:00:00"),
            "fcst": -2.2114694534118106e40,
            "fcst_lower": -1.0480171379102986e42,
            "fcst_upper": 1.0037877488420623e42,
        },
        {
            "time": pd.Timestamp("1962-08-01 00:00:00"),
            "fcst": 2.4263898227947306e42,
            "fcst_lower": -1.1013402759322984e44,
            "fcst_upper": 1.1498680723881932e44,
        },
        {
            "time": pd.Timestamp("1962-09-01 00:00:00"),
            "fcst": -2.6621971029619837e44,
            "fcst_lower": -1.2616173305468483e46,
            "fcst_upper": 1.2083733884876087e46,
        },
        {
            "time": pd.Timestamp("1962-10-01 00:00:00"),
            "fcst": 2.9209211761595633e46,
            "fcst_lower": -1.3258084516785478e48,
            "fcst_upper": 1.3842268752017392e48,
        },
        {
            "time": pd.Timestamp("1962-11-01 00:00:00"),
            "fcst": -3.2047891975559714e48,
            "fcst_lower": -1.5187521569636682e50,
            "fcst_upper": 1.4546563730125486e50,
        },
        {
            "time": pd.Timestamp("1962-12-01 00:00:00"),
            "fcst": 3.5162447670961676e50,
            "fcst_lower": -1.5960263044537214e52,
            "fcst_upper": 1.6663511997956447e52,
        },
        {
            "time": pd.Timestamp("1963-01-01 00:00:00"),
            "fcst": -3.8579689645609656e52,
            "fcst_lower": -1.828294569544312e54,
            "fcst_upper": 1.751135190253093e54,
        },
        {
            "time": pd.Timestamp("1963-02-01 00:00:00"),
            "fcst": 4.232903428906417e54,
            "fcst_lower": -1.921318242679158e56,
            "fcst_upper": 2.0059763112572865e56,
        },
        {
            "time": pd.Timestamp("1963-03-01 00:00:00"),
            "fcst": -4.644275680555325e56,
            "fcst_lower": -2.2009259494373087e58,
            "fcst_upper": 2.108040435826202e58,
        },
        {
            "time": pd.Timestamp("1963-04-01 00:00:00"),
            "fcst": 5.095626904620905e58,
            "fcst_lower": -2.3129091164416752e60,
            "fcst_upper": 2.4148216545340934e60,
        },
        {
            "time": pd.Timestamp("1963-05-01 00:00:00"),
            "fcst": -5.590842434226837e60,
            "fcst_lower": -2.6495046890140163e62,
            "fcst_upper": 2.5376878403294794e62,
        },
        {
            "time": pd.Timestamp("1963-06-01 00:00:00"),
            "fcst": 6.13418519633099e62,
            "fcst_lower": -2.78431155343604e64,
            "fcst_upper": 2.90699525736266e64,
        },
    ]
)

AIR_FCST_15_SARIMA_PARAM_1_MODEL_1_INCL_HIST = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1949-01-01 00:00:00"),
            "fcst": np.nan,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("1949-02-01 00:00:00"),
            "fcst": np.nan,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("1949-03-01 00:00:00"),
            "fcst": np.nan,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("1949-04-01 00:00:00"),
            "fcst": 135.0085515088317,
            "fcst_lower": 71.80596294755452,
            "fcst_upper": 198.21114007010885,
        },
        {
            "time": pd.Timestamp("1949-05-01 00:00:00"),
            "fcst": 125.62306006134283,
            "fcst_lower": 63.15956900973017,
            "fcst_upper": 188.08655111295548,
        },
        {
            "time": pd.Timestamp("1949-06-01 00:00:00"),
            "fcst": 121.01284262480458,
            "fcst_lower": 59.03692143976053,
            "fcst_upper": 182.98876380984862,
        },
        {
            "time": pd.Timestamp("1949-07-01 00:00:00"),
            "fcst": 139.98060524169694,
            "fcst_lower": 78.33811846537051,
            "fcst_upper": 201.62309201802336,
        },
        {
            "time": pd.Timestamp("1949-08-01 00:00:00"),
            "fcst": 148.56955010352056,
            "fcst_lower": 87.1607308157933,
            "fcst_upper": 209.9783693912478,
        },
        {
            "time": pd.Timestamp("1949-09-01 00:00:00"),
            "fcst": 147.5180865351954,
            "fcst_lower": 86.27583411392447,
            "fcst_upper": 208.76033895646634,
        },
        {
            "time": pd.Timestamp("1949-10-01 00:00:00"),
            "fcst": 131.89147407336858,
            "fcst_lower": 70.76940341948509,
            "fcst_upper": 193.01354472725208,
        },
        {
            "time": pd.Timestamp("1949-11-01 00:00:00"),
            "fcst": 116.05089123243054,
            "fcst_lower": 55.01629378221911,
            "fcst_upper": 177.085488682642,
        },
        {
            "time": pd.Timestamp("1949-12-01 00:00:00"),
            "fcst": 100.79090654677762,
            "fcst_lower": 39.82038033164898,
            "fcst_upper": 161.76143276190626,
        },
        {
            "time": pd.Timestamp("1950-01-01 00:00:00"),
            "fcst": 126.13248036324374,
            "fcst_lower": 65.20910226614157,
            "fcst_upper": 187.05585846034592,
        },
        {
            "time": pd.Timestamp("1950-02-01 00:00:00"),
            "fcst": 106.85235461638639,
            "fcst_lower": 45.96378987448384,
            "fcst_upper": 167.74091935828895,
        },
        {
            "time": pd.Timestamp("1950-03-01 00:00:00"),
            "fcst": 137.26328054657446,
            "fcst_lower": 76.40048612084445,
            "fcst_upper": 198.12607497230448,
        },
        {
            "time": pd.Timestamp("1950-04-01 00:00:00"),
            "fcst": 137.10578739491186,
            "fcst_lower": 76.26210478110622,
            "fcst_upper": 197.9494700087175,
        },
        {
            "time": pd.Timestamp("1950-05-01 00:00:00"),
            "fcst": 136.0301562760974,
            "fcst_lower": 75.20066695643767,
            "fcst_upper": 196.85964559575712,
        },
        {
            "time": pd.Timestamp("1950-06-01 00:00:00"),
            "fcst": 120.23040140472432,
            "fcst_lower": 59.41146346957495,
            "fcst_upper": 181.04933933987368,
        },
        {
            "time": pd.Timestamp("1950-07-01 00:00:00"),
            "fcst": 162.43659552335512,
            "fcst_lower": 101.62550752905884,
            "fcst_upper": 223.2476835176514,
        },
        {
            "time": pd.Timestamp("1950-08-01 00:00:00"),
            "fcst": 166.56792220834384,
            "fcst_lower": 105.76267766375248,
            "fcst_upper": 227.37316675293522,
        },
        {
            "time": pd.Timestamp("1950-09-01 00:00:00"),
            "fcst": 172.9619223189896,
            "fcst_lower": 112.16102943817532,
            "fcst_upper": 233.76281519980387,
        },
        {
            "time": pd.Timestamp("1950-10-01 00:00:00"),
            "fcst": 150.77620057807434,
            "fcst_lower": 89.97854943367574,
            "fcst_upper": 211.57385172247294,
        },
        {
            "time": pd.Timestamp("1950-11-01 00:00:00"),
            "fcst": 129.51001770308469,
            "fcst_lower": 68.71478202858111,
            "fcst_upper": 190.30525337758826,
        },
        {
            "time": pd.Timestamp("1950-12-01 00:00:00"),
            "fcst": 109.62000466116207,
            "fcst_lower": 48.826569105723955,
            "fcst_upper": 170.41344021660018,
        },
        {
            "time": pd.Timestamp("1951-01-01 00:00:00"),
            "fcst": 153.89935886484943,
            "fcst_lower": 93.1072650149965,
            "fcst_upper": 214.69145271470236,
        },
        {
            "time": pd.Timestamp("1951-02-01 00:00:00"),
            "fcst": 134.9454456980926,
            "fcst_lower": 74.15435197547745,
            "fcst_upper": 195.73653942070777,
        },
        {
            "time": pd.Timestamp("1951-03-01 00:00:00"),
            "fcst": 160.62731874968088,
            "fcst_lower": 99.83697059052213,
            "fcst_upper": 221.41766690883964,
        },
        {
            "time": pd.Timestamp("1951-04-01 00:00:00"),
            "fcst": 179.72266723733324,
            "fcst_lower": 118.9328749022144,
            "fcst_upper": 240.5124595724521,
        },
        {
            "time": pd.Timestamp("1951-05-01 00:00:00"),
            "fcst": 155.67375787300605,
            "fcst_lower": 94.88437992615539,
            "fcst_upper": 216.46313581985672,
        },
        {
            "time": pd.Timestamp("1951-06-01 00:00:00"),
            "fcst": 181.82932035221148,
            "fcst_lower": 121.04025135703824,
            "fcst_upper": 242.61838934738472,
        },
        {
            "time": pd.Timestamp("1951-07-01 00:00:00"),
            "fcst": 171.84831446216,
            "fcst_lower": 111.05947581440104,
            "fcst_upper": 232.63715310991898,
        },
        {
            "time": pd.Timestamp("1951-08-01 00:00:00"),
            "fcst": 212.4867364260599,
            "fcst_lower": 151.69806952299996,
            "fcst_upper": 273.27540332911985,
        },
        {
            "time": pd.Timestamp("1951-09-01 00:00:00"),
            "fcst": 187.3544433748014,
            "fcst_lower": 126.5659045244358,
            "fcst_upper": 248.14298222516703,
        },
        {
            "time": pd.Timestamp("1951-10-01 00:00:00"),
            "fcst": 188.21643415300693,
            "fcst_lower": 127.4279907794978,
            "fcst_upper": 249.00487752651605,
        },
        {
            "time": pd.Timestamp("1951-11-01 00:00:00"),
            "fcst": 149.7947340852945,
            "fcst_lower": 89.00636190039214,
            "fcst_upper": 210.58310627019685,
        },
        {
            "time": pd.Timestamp("1951-12-01 00:00:00"),
            "fcst": 150.31042796244319,
            "fcst_lower": 89.52210885683084,
            "fcst_upper": 211.09874706805553,
        },
        {
            "time": pd.Timestamp("1952-01-01 00:00:00"),
            "fcst": 170.06388969156714,
            "fcst_lower": 109.27561016281888,
            "fcst_upper": 230.8521692203154,
        },
        {
            "time": pd.Timestamp("1952-02-01 00:00:00"),
            "fcst": 169.43734205976682,
            "fcst_lower": 108.64909204031761,
            "fcst_upper": 230.22559207921603,
        },
        {
            "time": pd.Timestamp("1952-03-01 00:00:00"),
            "fcst": 184.8530229780305,
            "fcst_lower": 124.06479496134995,
            "fcst_upper": 245.64125099471107,
        },
        {
            "time": pd.Timestamp("1952-04-01 00:00:00"),
            "fcst": 193.87031577249965,
            "fcst_lower": 133.0821041615831,
            "fcst_upper": 254.6585273834162,
        },
        {
            "time": pd.Timestamp("1952-05-01 00:00:00"),
            "fcst": 175.57690391180074,
            "fcst_lower": 114.78870453340889,
            "fcst_upper": 236.3651032901926,
        },
        {
            "time": pd.Timestamp("1952-06-01 00:00:00"),
            "fcst": 188.4614166915688,
            "fcst_lower": 127.67322643404401,
            "fcst_upper": 249.24960694909356,
        },
        {
            "time": pd.Timestamp("1952-07-01 00:00:00"),
            "fcst": 226.90956070992576,
            "fcst_lower": 166.12137725314514,
            "fcst_upper": 287.6977441667064,
        },
        {
            "time": pd.Timestamp("1952-08-01 00:00:00"),
            "fcst": 226.97822295549526,
            "fcst_lower": 166.1900445695202,
            "fcst_upper": 287.76640134147027,
        },
        {
            "time": pd.Timestamp("1952-09-01 00:00:00"),
            "fcst": 249.28088657412243,
            "fcst_lower": 188.49271196906875,
            "fcst_upper": 310.0690611791761,
        },
        {
            "time": pd.Timestamp("1952-10-01 00:00:00"),
            "fcst": 189.8660953003974,
            "fcst_lower": 129.0779235144954,
            "fcst_upper": 250.6542670862994,
        },
        {
            "time": pd.Timestamp("1952-11-01 00:00:00"),
            "fcst": 200.51466059348994,
            "fcst_lower": 139.72649090962,
            "fcst_upper": 261.3028302773599,
        },
        {
            "time": pd.Timestamp("1952-12-01 00:00:00"),
            "fcst": 156.38743049987673,
            "fcst_lower": 95.59926238333622,
            "fcst_upper": 217.17559861641723,
        },
        {
            "time": pd.Timestamp("1953-01-01 00:00:00"),
            "fcst": 216.04599225409171,
            "fcst_lower": 155.25782530619267,
            "fcst_upper": 276.83415920199076,
        },
        {
            "time": pd.Timestamp("1953-02-01 00:00:00"),
            "fcst": 177.7419649757186,
            "fcst_lower": 116.953798899189,
            "fcst_upper": 238.53013105224818,
        },
        {
            "time": pd.Timestamp("1953-03-01 00:00:00"),
            "fcst": 211.7657477407197,
            "fcst_lower": 150.97758231390586,
            "fcst_upper": 272.55391316753355,
        },
        {
            "time": pd.Timestamp("1953-04-01 00:00:00"),
            "fcst": 237.95832780274853,
            "fcst_lower": 177.17016286037972,
            "fcst_upper": 298.74649274511734,
        },
        {
            "time": pd.Timestamp("1953-05-01 00:00:00"),
            "fcst": 232.91969102352166,
            "fcst_lower": 172.1315264423677,
            "fcst_upper": 293.7078556046756,
        },
        {
            "time": pd.Timestamp("1953-06-01 00:00:00"),
            "fcst": 228.4605400761027,
            "fcst_lower": 167.67237576427993,
            "fcst_upper": 289.24870438792544,
        },
        {
            "time": pd.Timestamp("1953-07-01 00:00:00"),
            "fcst": 248.91602032670454,
            "fcst_lower": 188.1278562157022,
            "fcst_upper": 309.7041844377069,
        },
        {
            "time": pd.Timestamp("1953-08-01 00:00:00"),
            "fcst": 267.066835347853,
            "fcst_lower": 206.27867138658758,
            "fcst_upper": 327.8549993091184,
        },
        {
            "time": pd.Timestamp("1953-09-01 00:00:00"),
            "fcst": 272.46619776983084,
            "fcst_lower": 211.67803392021324,
            "fcst_upper": 333.25436161944845,
        },
        {
            "time": pd.Timestamp("1953-10-01 00:00:00"),
            "fcst": 222.97194293233372,
            "fcst_lower": 162.18377916596359,
            "fcst_upper": 283.76010669870385,
        },
        {
            "time": pd.Timestamp("1953-11-01 00:00:00"),
            "fcst": 212.9913800856689,
            "fcst_lower": 152.2032163813703,
            "fcst_upper": 273.77954378996753,
        },
        {
            "time": pd.Timestamp("1953-12-01 00:00:00"),
            "fcst": 166.2121528915526,
            "fcst_lower": 105.42398923353613,
            "fcst_upper": 227.00031654956905,
        },
        {
            "time": pd.Timestamp("1954-01-01 00:00:00"),
            "fcst": 221.08105573624715,
            "fcst_lower": 160.29289211273988,
            "fcst_upper": 281.8692193597544,
        },
        {
            "time": pd.Timestamp("1954-02-01 00:00:00"),
            "fcst": 187.82797877201423,
            "fcst_lower": 127.03981517423793,
            "fcst_upper": 248.61614236979054,
        },
        {
            "time": pd.Timestamp("1954-03-01 00:00:00"),
            "fcst": 195.73568473628217,
            "fcst_lower": 134.94752115769154,
            "fcst_upper": 256.5238483148728,
        },
        {
            "time": pd.Timestamp("1954-04-01 00:00:00"),
            "fcst": 246.6173596818791,
            "fcst_lower": 185.8291961175938,
            "fcst_upper": 307.4055232461644,
        },
        {
            "time": pd.Timestamp("1954-05-01 00:00:00"),
            "fcst": 213.8540513995003,
            "fcst_lower": 153.0658878458814,
            "fcst_upper": 274.64221495311915,
        },
        {
            "time": pd.Timestamp("1954-06-01 00:00:00"),
            "fcst": 248.0765798423727,
            "fcst_lower": 187.288416296707,
            "fcst_upper": 308.8647433880384,
        },
        {
            "time": pd.Timestamp("1954-07-01 00:00:00"),
            "fcst": 263.52392042845304,
            "fcst_lower": 202.73575688871742,
            "fcst_upper": 324.31208396818863,
        },
        {
            "time": pd.Timestamp("1954-08-01 00:00:00"),
            "fcst": 317.204490044766,
            "fcst_lower": 256.416326509452,
            "fcst_upper": 377.99265358007995,
        },
        {
            "time": pd.Timestamp("1954-09-01 00:00:00"),
            "fcst": 276.36727700385717,
            "fcst_lower": 215.57911347184006,
            "fcst_upper": 337.1554405358743,
        },
        {
            "time": pd.Timestamp("1954-10-01 00:00:00"),
            "fcst": 260.1261005070125,
            "fcst_lower": 199.33793697745364,
            "fcst_upper": 320.91426403657135,
        },
        {
            "time": pd.Timestamp("1954-11-01 00:00:00"),
            "fcst": 216.34861852382815,
            "fcst_lower": 155.56045499610224,
            "fcst_upper": 277.13678205155406,
        },
        {
            "time": pd.Timestamp("1954-12-01 00:00:00"),
            "fcst": 203.8026254705358,
            "fcst_lower": 143.01446194417656,
            "fcst_upper": 264.590788996895,
        },
        {
            "time": pd.Timestamp("1955-01-01 00:00:00"),
            "fcst": 238.42873369055582,
            "fcst_lower": 177.6405701652156,
            "fcst_upper": 299.21689721589604,
        },
        {
            "time": pd.Timestamp("1955-02-01 00:00:00"),
            "fcst": 238.91922058333466,
            "fcst_lower": 178.13105705875427,
            "fcst_upper": 299.70738410791506,
        },
        {
            "time": pd.Timestamp("1955-03-01 00:00:00"),
            "fcst": 232.15654290166714,
            "fcst_lower": 171.3683793776533,
            "fcst_upper": 292.944706425681,
        },
        {
            "time": pd.Timestamp("1955-04-01 00:00:00"),
            "fcst": 280.96451957808154,
            "fcst_lower": 220.1763560544901,
            "fcst_upper": 341.752683101673,
        },
        {
            "time": pd.Timestamp("1955-05-01 00:00:00"),
            "fcst": 257.7202862742384,
            "fcst_lower": 196.93212275096192,
            "fcst_upper": 318.50844979751486,
        },
        {
            "time": pd.Timestamp("1955-06-01 00:00:00"),
            "fcst": 280.1292930749069,
            "fcst_lower": 219.34112955186532,
            "fcst_upper": 340.9174565979485,
        },
        {
            "time": pd.Timestamp("1955-07-01 00:00:00"),
            "fcst": 323.77188751363974,
            "fcst_lower": 262.9837239907733,
            "fcst_upper": 384.5600510365062,
        },
        {
            "time": pd.Timestamp("1955-08-01 00:00:00"),
            "fcst": 375.5012021316152,
            "fcst_lower": 314.71303860887923,
            "fcst_upper": 436.2893656543511,
        },
        {
            "time": pd.Timestamp("1955-09-01 00:00:00"),
            "fcst": 330.45065371688304,
            "fcst_lower": 269.66249019424447,
            "fcst_upper": 391.2388172395216,
        },
        {
            "time": pd.Timestamp("1955-10-01 00:00:00"),
            "fcst": 312.6648050820013,
            "fcst_lower": 251.8766415594353,
            "fcst_upper": 373.4529686045673,
        },
        {
            "time": pd.Timestamp("1955-11-01 00:00:00"),
            "fcst": 258.6325461215688,
            "fcst_lower": 197.84438259905693,
            "fcst_upper": 319.42070964408066,
        },
        {
            "time": pd.Timestamp("1955-12-01 00:00:00"),
            "fcst": 235.86564530137574,
            "fcst_lower": 175.07748177890426,
            "fcst_upper": 296.6538088238472,
        },
        {
            "time": pd.Timestamp("1956-01-01 00:00:00"),
            "fcst": 294.94080808729143,
            "fcst_lower": 234.15264456485005,
            "fcst_upper": 355.7289716097328,
        },
        {
            "time": pd.Timestamp("1956-02-01 00:00:00"),
            "fcst": 271.70747129501024,
            "fcst_lower": 210.91930777259125,
            "fcst_upper": 332.4956348174292,
        },
        {
            "time": pd.Timestamp("1956-03-01 00:00:00"),
            "fcst": 284.8894558755466,
            "fcst_lower": 224.1012923531444,
            "fcst_upper": 345.67761939794883,
        },
        {
            "time": pd.Timestamp("1956-04-01 00:00:00"),
            "fcst": 325.7594794539503,
            "fcst_lower": 264.9713159315605,
            "fcst_upper": 386.54764297634006,
        },
        {
            "time": pd.Timestamp("1956-05-01 00:00:00"),
            "fcst": 303.8790205944456,
            "fcst_lower": 243.09085707205583,
            "fcst_upper": 364.66718411683536,
        },
        {
            "time": pd.Timestamp("1956-06-01 00:00:00"),
            "fcst": 327.8224332648391,
            "fcst_lower": 267.03426974244934,
            "fcst_upper": 388.61059678722887,
        },
        {
            "time": pd.Timestamp("1956-07-01 00:00:00"),
            "fcst": 387.3191590210848,
            "fcst_lower": 326.530995498695,
            "fcst_upper": 448.10732254347454,
        },
        {
            "time": pd.Timestamp("1956-08-01 00:00:00"),
            "fcst": 416.6816499516949,
            "fcst_lower": 355.89348642930514,
            "fcst_upper": 477.4698134740847,
        },
        {
            "time": pd.Timestamp("1956-09-01 00:00:00"),
            "fcst": 398.70650883375663,
            "fcst_lower": 337.91834531136686,
            "fcst_upper": 459.4946723561464,
        },
        {
            "time": pd.Timestamp("1956-10-01 00:00:00"),
            "fcst": 340.96941141178087,
            "fcst_lower": 280.1812478893911,
            "fcst_upper": 401.75757493417063,
        },
        {
            "time": pd.Timestamp("1956-11-01 00:00:00"),
            "fcst": 299.0396677611477,
            "fcst_lower": 238.25150423875795,
            "fcst_upper": 359.8278312835375,
        },
        {
            "time": pd.Timestamp("1956-12-01 00:00:00"),
            "fcst": 263.38472516050194,
            "fcst_lower": 202.59656163811218,
            "fcst_upper": 324.1728886828917,
        },
        {
            "time": pd.Timestamp("1957-01-01 00:00:00"),
            "fcst": 326.2012603176815,
            "fcst_lower": 265.4130967952917,
            "fcst_upper": 386.98942384007125,
        },
        {
            "time": pd.Timestamp("1957-02-01 00:00:00"),
            "fcst": 301.059982020002,
            "fcst_lower": 240.27181849761223,
            "fcst_upper": 361.84814554239176,
        },
        {
            "time": pd.Timestamp("1957-03-01 00:00:00"),
            "fcst": 307.58695762114746,
            "fcst_lower": 246.7987940987577,
            "fcst_upper": 368.3751211435372,
        },
        {
            "time": pd.Timestamp("1957-04-01 00:00:00"),
            "fcst": 371.7236809447925,
            "fcst_lower": 310.9355174224027,
            "fcst_upper": 432.51184446718224,
        },
        {
            "time": pd.Timestamp("1957-05-01 00:00:00"),
            "fcst": 331.30825739954514,
            "fcst_lower": 270.5200938771554,
            "fcst_upper": 392.0964209219349,
        },
        {
            "time": pd.Timestamp("1957-06-01 00:00:00"),
            "fcst": 372.1383605155684,
            "fcst_lower": 311.35019699317866,
            "fcst_upper": 432.9265240379582,
        },
        {
            "time": pd.Timestamp("1957-07-01 00:00:00"),
            "fcst": 433.28418069535934,
            "fcst_lower": 372.4960171729696,
            "fcst_upper": 494.0723442177491,
        },
        {
            "time": pd.Timestamp("1957-08-01 00:00:00"),
            "fcst": 471.99604626997774,
            "fcst_lower": 411.207882747588,
            "fcst_upper": 532.7842097923675,
        },
        {
            "time": pd.Timestamp("1957-09-01 00:00:00"),
            "fcst": 461.7375384808529,
            "fcst_lower": 400.9493749584631,
            "fcst_upper": 522.5257020032426,
        },
        {
            "time": pd.Timestamp("1957-10-01 00:00:00"),
            "fcst": 384.0182219526643,
            "fcst_lower": 323.23005843027454,
            "fcst_upper": 444.80638547505407,
        },
        {
            "time": pd.Timestamp("1957-11-01 00:00:00"),
            "fcst": 342.0640995662381,
            "fcst_lower": 281.27593604384833,
            "fcst_upper": 402.85226308862786,
        },
        {
            "time": pd.Timestamp("1957-12-01 00:00:00"),
            "fcst": 292.9115359604155,
            "fcst_lower": 232.12337243802574,
            "fcst_upper": 353.6996994828053,
        },
        {
            "time": pd.Timestamp("1958-01-01 00:00:00"),
            "fcst": 358.5066437814099,
            "fcst_lower": 297.7184802590201,
            "fcst_upper": 419.29480730379964,
        },
        {
            "time": pd.Timestamp("1958-02-01 00:00:00"),
            "fcst": 322.1227918937741,
            "fcst_lower": 261.3346283713843,
            "fcst_upper": 382.91095541616386,
        },
        {
            "time": pd.Timestamp("1958-03-01 00:00:00"),
            "fcst": 324.8723080815947,
            "fcst_lower": 264.08414455920496,
            "fcst_upper": 385.6604716039845,
        },
        {
            "time": pd.Timestamp("1958-04-01 00:00:00"),
            "fcst": 373.1949826857821,
            "fcst_lower": 312.40681916339236,
            "fcst_upper": 433.9831462081719,
        },
        {
            "time": pd.Timestamp("1958-05-01 00:00:00"),
            "fcst": 332.8829728970037,
            "fcst_lower": 272.09480937461393,
            "fcst_upper": 393.67113641939346,
        },
        {
            "time": pd.Timestamp("1958-06-01 00:00:00"),
            "fcst": 381.89299859284955,
            "fcst_lower": 321.1048350704598,
            "fcst_upper": 442.6811621152393,
        },
        {
            "time": pd.Timestamp("1958-07-01 00:00:00"),
            "fcst": 446.7155564920999,
            "fcst_lower": 385.9273929697101,
            "fcst_upper": 507.50372001448966,
        },
        {
            "time": pd.Timestamp("1958-08-01 00:00:00"),
            "fcst": 502.6844537687679,
            "fcst_lower": 441.89629024637816,
            "fcst_upper": 563.4726172911577,
        },
        {
            "time": pd.Timestamp("1958-09-01 00:00:00"),
            "fcst": 500.3607141468311,
            "fcst_lower": 439.57255062444136,
            "fcst_upper": 561.1488776692208,
        },
        {
            "time": pd.Timestamp("1958-10-01 00:00:00"),
            "fcst": 368.68671492844595,
            "fcst_lower": 307.8985514060562,
            "fcst_upper": 429.4748784508357,
        },
        {
            "time": pd.Timestamp("1958-11-01 00:00:00"),
            "fcst": 371.974401051855,
            "fcst_lower": 311.18623752946525,
            "fcst_upper": 432.7625645742448,
        },
        {
            "time": pd.Timestamp("1958-12-01 00:00:00"),
            "fcst": 279.72095413080376,
            "fcst_lower": 218.932790608414,
            "fcst_upper": 340.5091176531935,
        },
        {
            "time": pd.Timestamp("1959-01-01 00:00:00"),
            "fcst": 373.656947563427,
            "fcst_lower": 312.86878404103726,
            "fcst_upper": 434.4451110858168,
        },
        {
            "time": pd.Timestamp("1959-02-01 00:00:00"),
            "fcst": 337.3007528305821,
            "fcst_lower": 276.51258930819233,
            "fcst_upper": 398.08891635297186,
        },
        {
            "time": pd.Timestamp("1959-03-01 00:00:00"),
            "fcst": 354.59332155002505,
            "fcst_lower": 293.8051580276353,
            "fcst_upper": 415.3814850724148,
        },
        {
            "time": pd.Timestamp("1959-04-01 00:00:00"),
            "fcst": 420.0409061007712,
            "fcst_lower": 359.25274257838146,
            "fcst_upper": 480.829069623161,
        },
        {
            "time": pd.Timestamp("1959-05-01 00:00:00"),
            "fcst": 379.98272771878794,
            "fcst_lower": 319.1945641963982,
            "fcst_upper": 440.7708912411777,
        },
        {
            "time": pd.Timestamp("1959-06-01 00:00:00"),
            "fcst": 443.1740564357343,
            "fcst_lower": 382.38589291334455,
            "fcst_upper": 503.9622199581241,
        },
        {
            "time": pd.Timestamp("1959-07-01 00:00:00"),
            "fcst": 472.2328796575603,
            "fcst_lower": 411.44471613517055,
            "fcst_upper": 533.0210431799501,
        },
        {
            "time": pd.Timestamp("1959-08-01 00:00:00"),
            "fcst": 577.3857032885581,
            "fcst_lower": 516.5975397661684,
            "fcst_upper": 638.1738668109479,
        },
        {
            "time": pd.Timestamp("1959-09-01 00:00:00"),
            "fcst": 537.9078476419501,
            "fcst_lower": 477.1196841195603,
            "fcst_upper": 598.6960111643398,
        },
        {
            "time": pd.Timestamp("1959-10-01 00:00:00"),
            "fcst": 443.840206931614,
            "fcst_lower": 383.0520434092242,
            "fcst_upper": 504.62837045400374,
        },
        {
            "time": pd.Timestamp("1959-11-01 00:00:00"),
            "fcst": 401.74361834799936,
            "fcst_lower": 340.9554548256096,
            "fcst_upper": 462.5317818703891,
        },
        {
            "time": pd.Timestamp("1959-12-01 00:00:00"),
            "fcst": 349.02037060055704,
            "fcst_lower": 288.2322070781673,
            "fcst_upper": 409.8085341229468,
        },
        {
            "time": pd.Timestamp("1960-01-01 00:00:00"),
            "fcst": 432.9477609983596,
            "fcst_lower": 372.15959747596986,
            "fcst_upper": 493.7359245207494,
        },
        {
            "time": pd.Timestamp("1960-02-01 00:00:00"),
            "fcst": 397.5388056768658,
            "fcst_lower": 336.75064215447605,
            "fcst_upper": 458.3269691992556,
        },
        {
            "time": pd.Timestamp("1960-03-01 00:00:00"),
            "fcst": 397.6828749250387,
            "fcst_lower": 336.8947114026489,
            "fcst_upper": 458.47103844742844,
        },
        {
            "time": pd.Timestamp("1960-04-01 00:00:00"),
            "fcst": 424.1297588023324,
            "fcst_lower": 363.3415952799426,
            "fcst_upper": 484.91792232472216,
        },
        {
            "time": pd.Timestamp("1960-05-01 00:00:00"),
            "fcst": 472.9210680197455,
            "fcst_lower": 412.13290449735575,
            "fcst_upper": 533.7092315421353,
        },
        {
            "time": pd.Timestamp("1960-06-01 00:00:00"),
            "fcst": 465.9884988075222,
            "fcst_lower": 405.20033528513244,
            "fcst_upper": 526.7766623299119,
        },
        {
            "time": pd.Timestamp("1960-07-01 00:00:00"),
            "fcst": 564.7168052202932,
            "fcst_lower": 503.9286416979034,
            "fcst_upper": 625.5049687426829,
        },
        {
            "time": pd.Timestamp("1960-08-01 00:00:00"),
            "fcst": 630.2087363420145,
            "fcst_lower": 569.4205728196248,
            "fcst_upper": 690.9968998644042,
        },
        {
            "time": pd.Timestamp("1960-09-01 00:00:00"),
            "fcst": 592.6829862745113,
            "fcst_lower": 531.8948227521216,
            "fcst_upper": 653.471149796901,
        },
        {
            "time": pd.Timestamp("1960-10-01 00:00:00"),
            "fcst": 481.3478022088564,
            "fcst_lower": 420.55963868646666,
            "fcst_upper": 542.1359657312462,
        },
        {
            "time": pd.Timestamp("1960-11-01 00:00:00"),
            "fcst": 465.71698278583256,
            "fcst_lower": 404.9288192634428,
            "fcst_upper": 526.5051463082223,
        },
        {
            "time": pd.Timestamp("1960-12-01 00:00:00"),
            "fcst": 358.28660749941486,
            "fcst_lower": 297.4984439770251,
            "fcst_upper": 419.0747710218046,
        },
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 475.734999664335,
            "fcst_lower": 414.94683614194525,
            "fcst_upper": 536.5231631867248,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 454.9960131326432,
            "fcst_lower": 350.9406985933037,
            "fcst_upper": 559.0513276719827,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 464.83036926989587,
            "fcst_lower": 337.5891858591327,
            "fcst_upper": 592.071552680659,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 460.16695138848075,
            "fcst_lower": 310.6316031682349,
            "fcst_upper": 609.7022996087267,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 462.3783281205398,
            "fcst_lower": 294.62476708969064,
            "fcst_upper": 630.1318891513889,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 461.3297008742918,
            "fcst_lower": 276.654109966928,
            "fcst_upper": 646.0052917816556,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 461.82695631052394,
            "fcst_lower": 261.87302294007793,
            "fcst_upper": 661.78088968097,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 461.591159491638,
            "fcst_lower": 247.3497039224739,
            "fcst_upper": 675.832615060802,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 461.7029735327635,
            "fcst_lower": 234.11208345911712,
            "fcst_upper": 689.2938636064099,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 461.64995169874607,
            "fcst_lower": 221.43104743037904,
            "fcst_upper": 701.8688559671131,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 461.67509447021087,
            "fcst_lower": 209.4683909799995,
            "fcst_upper": 713.8817979604222,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 461.66317185439334,
            "fcst_lower": 198.00921052859343,
            "fcst_upper": 725.3171331801932,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 461.6688255178382,
            "fcst_lower": 187.04615729497976,
            "fcst_upper": 736.2914937406965,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 461.66614457006324,
            "fcst_lower": 176.49553139848467,
            "fcst_upper": 746.8367577416418,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 461.6674158662842,
            "fcst_lower": 166.32570802200962,
            "fcst_upper": 757.0091237105588,
        },
    ]
)

AIR_FCST_30_SARIMA_PARAM_1_MODEL_1_INCL_HIST = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1949-01-01 00:00:00"),
            "fcst": np.nan,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("1949-02-01 00:00:00"),
            "fcst": np.nan,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("1949-03-01 00:00:00"),
            "fcst": np.nan,
            "fcst_lower": np.nan,
            "fcst_upper": np.nan,
        },
        {
            "time": pd.Timestamp("1949-04-01 00:00:00"),
            "fcst": 135.0085515088317,
            "fcst_lower": 71.80596294755452,
            "fcst_upper": 198.21114007010885,
        },
        {
            "time": pd.Timestamp("1949-05-01 00:00:00"),
            "fcst": 125.62306006134283,
            "fcst_lower": 63.15956900973017,
            "fcst_upper": 188.08655111295548,
        },
        {
            "time": pd.Timestamp("1949-06-01 00:00:00"),
            "fcst": 121.01284262480458,
            "fcst_lower": 59.03692143976053,
            "fcst_upper": 182.98876380984862,
        },
        {
            "time": pd.Timestamp("1949-07-01 00:00:00"),
            "fcst": 139.98060524169694,
            "fcst_lower": 78.33811846537051,
            "fcst_upper": 201.62309201802336,
        },
        {
            "time": pd.Timestamp("1949-08-01 00:00:00"),
            "fcst": 148.56955010352056,
            "fcst_lower": 87.1607308157933,
            "fcst_upper": 209.9783693912478,
        },
        {
            "time": pd.Timestamp("1949-09-01 00:00:00"),
            "fcst": 147.5180865351954,
            "fcst_lower": 86.27583411392447,
            "fcst_upper": 208.76033895646634,
        },
        {
            "time": pd.Timestamp("1949-10-01 00:00:00"),
            "fcst": 131.89147407336858,
            "fcst_lower": 70.76940341948509,
            "fcst_upper": 193.01354472725208,
        },
        {
            "time": pd.Timestamp("1949-11-01 00:00:00"),
            "fcst": 116.05089123243054,
            "fcst_lower": 55.01629378221911,
            "fcst_upper": 177.085488682642,
        },
        {
            "time": pd.Timestamp("1949-12-01 00:00:00"),
            "fcst": 100.79090654677762,
            "fcst_lower": 39.82038033164898,
            "fcst_upper": 161.76143276190626,
        },
        {
            "time": pd.Timestamp("1950-01-01 00:00:00"),
            "fcst": 126.13248036324374,
            "fcst_lower": 65.20910226614157,
            "fcst_upper": 187.05585846034592,
        },
        {
            "time": pd.Timestamp("1950-02-01 00:00:00"),
            "fcst": 106.85235461638639,
            "fcst_lower": 45.96378987448384,
            "fcst_upper": 167.74091935828895,
        },
        {
            "time": pd.Timestamp("1950-03-01 00:00:00"),
            "fcst": 137.26328054657446,
            "fcst_lower": 76.40048612084445,
            "fcst_upper": 198.12607497230448,
        },
        {
            "time": pd.Timestamp("1950-04-01 00:00:00"),
            "fcst": 137.10578739491186,
            "fcst_lower": 76.26210478110622,
            "fcst_upper": 197.9494700087175,
        },
        {
            "time": pd.Timestamp("1950-05-01 00:00:00"),
            "fcst": 136.0301562760974,
            "fcst_lower": 75.20066695643767,
            "fcst_upper": 196.85964559575712,
        },
        {
            "time": pd.Timestamp("1950-06-01 00:00:00"),
            "fcst": 120.23040140472432,
            "fcst_lower": 59.41146346957495,
            "fcst_upper": 181.04933933987368,
        },
        {
            "time": pd.Timestamp("1950-07-01 00:00:00"),
            "fcst": 162.43659552335512,
            "fcst_lower": 101.62550752905884,
            "fcst_upper": 223.2476835176514,
        },
        {
            "time": pd.Timestamp("1950-08-01 00:00:00"),
            "fcst": 166.56792220834384,
            "fcst_lower": 105.76267766375248,
            "fcst_upper": 227.37316675293522,
        },
        {
            "time": pd.Timestamp("1950-09-01 00:00:00"),
            "fcst": 172.9619223189896,
            "fcst_lower": 112.16102943817532,
            "fcst_upper": 233.76281519980387,
        },
        {
            "time": pd.Timestamp("1950-10-01 00:00:00"),
            "fcst": 150.77620057807434,
            "fcst_lower": 89.97854943367574,
            "fcst_upper": 211.57385172247294,
        },
        {
            "time": pd.Timestamp("1950-11-01 00:00:00"),
            "fcst": 129.51001770308469,
            "fcst_lower": 68.71478202858111,
            "fcst_upper": 190.30525337758826,
        },
        {
            "time": pd.Timestamp("1950-12-01 00:00:00"),
            "fcst": 109.62000466116207,
            "fcst_lower": 48.826569105723955,
            "fcst_upper": 170.41344021660018,
        },
        {
            "time": pd.Timestamp("1951-01-01 00:00:00"),
            "fcst": 153.89935886484943,
            "fcst_lower": 93.1072650149965,
            "fcst_upper": 214.69145271470236,
        },
        {
            "time": pd.Timestamp("1951-02-01 00:00:00"),
            "fcst": 134.9454456980926,
            "fcst_lower": 74.15435197547745,
            "fcst_upper": 195.73653942070777,
        },
        {
            "time": pd.Timestamp("1951-03-01 00:00:00"),
            "fcst": 160.62731874968088,
            "fcst_lower": 99.83697059052213,
            "fcst_upper": 221.41766690883964,
        },
        {
            "time": pd.Timestamp("1951-04-01 00:00:00"),
            "fcst": 179.72266723733324,
            "fcst_lower": 118.9328749022144,
            "fcst_upper": 240.5124595724521,
        },
        {
            "time": pd.Timestamp("1951-05-01 00:00:00"),
            "fcst": 155.67375787300605,
            "fcst_lower": 94.88437992615539,
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        {
            "time": pd.Timestamp("1959-11-01 00:00:00"),
            "fcst": 401.74361834799936,
            "fcst_lower": 340.9554548256096,
            "fcst_upper": 462.5317818703891,
        },
        {
            "time": pd.Timestamp("1959-12-01 00:00:00"),
            "fcst": 349.02037060055704,
            "fcst_lower": 288.2322070781673,
            "fcst_upper": 409.8085341229468,
        },
        {
            "time": pd.Timestamp("1960-01-01 00:00:00"),
            "fcst": 432.9477609983596,
            "fcst_lower": 372.15959747596986,
            "fcst_upper": 493.7359245207494,
        },
        {
            "time": pd.Timestamp("1960-02-01 00:00:00"),
            "fcst": 397.5388056768658,
            "fcst_lower": 336.75064215447605,
            "fcst_upper": 458.3269691992556,
        },
        {
            "time": pd.Timestamp("1960-03-01 00:00:00"),
            "fcst": 397.6828749250387,
            "fcst_lower": 336.8947114026489,
            "fcst_upper": 458.47103844742844,
        },
        {
            "time": pd.Timestamp("1960-04-01 00:00:00"),
            "fcst": 424.1297588023324,
            "fcst_lower": 363.3415952799426,
            "fcst_upper": 484.91792232472216,
        },
        {
            "time": pd.Timestamp("1960-05-01 00:00:00"),
            "fcst": 472.9210680197455,
            "fcst_lower": 412.13290449735575,
            "fcst_upper": 533.7092315421353,
        },
        {
            "time": pd.Timestamp("1960-06-01 00:00:00"),
            "fcst": 465.9884988075222,
            "fcst_lower": 405.20033528513244,
            "fcst_upper": 526.7766623299119,
        },
        {
            "time": pd.Timestamp("1960-07-01 00:00:00"),
            "fcst": 564.7168052202932,
            "fcst_lower": 503.9286416979034,
            "fcst_upper": 625.5049687426829,
        },
        {
            "time": pd.Timestamp("1960-08-01 00:00:00"),
            "fcst": 630.2087363420145,
            "fcst_lower": 569.4205728196248,
            "fcst_upper": 690.9968998644042,
        },
        {
            "time": pd.Timestamp("1960-09-01 00:00:00"),
            "fcst": 592.6829862745113,
            "fcst_lower": 531.8948227521216,
            "fcst_upper": 653.471149796901,
        },
        {
            "time": pd.Timestamp("1960-10-01 00:00:00"),
            "fcst": 481.3478022088564,
            "fcst_lower": 420.55963868646666,
            "fcst_upper": 542.1359657312462,
        },
        {
            "time": pd.Timestamp("1960-11-01 00:00:00"),
            "fcst": 465.71698278583256,
            "fcst_lower": 404.9288192634428,
            "fcst_upper": 526.5051463082223,
        },
        {
            "time": pd.Timestamp("1960-12-01 00:00:00"),
            "fcst": 358.28660749941486,
            "fcst_lower": 297.4984439770251,
            "fcst_upper": 419.0747710218046,
        },
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 475.734999664335,
            "fcst_lower": 414.94683614194525,
            "fcst_upper": 536.5231631867248,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 454.9960131326432,
            "fcst_lower": 350.9406985933037,
            "fcst_upper": 559.0513276719827,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 464.83036926989587,
            "fcst_lower": 337.5891858591327,
            "fcst_upper": 592.071552680659,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 460.16695138848075,
            "fcst_lower": 310.6316031682349,
            "fcst_upper": 609.7022996087267,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 462.3783281205398,
            "fcst_lower": 294.62476708969064,
            "fcst_upper": 630.1318891513889,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 461.3297008742918,
            "fcst_lower": 276.654109966928,
            "fcst_upper": 646.0052917816556,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 461.82695631052394,
            "fcst_lower": 261.87302294007793,
            "fcst_upper": 661.78088968097,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 461.591159491638,
            "fcst_lower": 247.3497039224739,
            "fcst_upper": 675.832615060802,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 461.7029735327635,
            "fcst_lower": 234.11208345911712,
            "fcst_upper": 689.2938636064099,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 461.64995169874607,
            "fcst_lower": 221.43104743037904,
            "fcst_upper": 701.8688559671131,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 461.67509447021087,
            "fcst_lower": 209.4683909799995,
            "fcst_upper": 713.8817979604222,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 461.66317185439334,
            "fcst_lower": 198.00921052859343,
            "fcst_upper": 725.3171331801932,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 461.6688255178382,
            "fcst_lower": 187.04615729497976,
            "fcst_upper": 736.2914937406965,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 461.66614457006324,
            "fcst_lower": 176.49553139848467,
            "fcst_upper": 746.8367577416418,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 461.6674158662842,
            "fcst_lower": 166.32570802200962,
            "fcst_upper": 757.0091237105588,
        },
        {
            "time": pd.Timestamp("1962-04-01 00:00:00"),
            "fcst": 461.66681302198543,
            "fcst_lower": 156.49263999202753,
            "fcst_upper": 766.8409860519433,
        },
        {
            "time": pd.Timestamp("1962-05-01 00:00:00"),
            "fcst": 461.66709888868,
            "fcst_lower": 146.96759579695265,
            "fcst_upper": 776.3666019804074,
        },
        {
            "time": pd.Timestamp("1962-06-01 00:00:00"),
            "fcst": 461.6669633316759,
            "fcst_lower": 137.72205693628518,
            "fcst_upper": 785.6118697270666,
        },
        {
            "time": pd.Timestamp("1962-07-01 00:00:00"),
            "fcst": 461.66702761234137,
            "fcst_lower": 128.7333762035309,
            "fcst_upper": 794.6006790211518,
        },
        {
            "time": pd.Timestamp("1962-08-01 00:00:00"),
            "fcst": 461.6669971306707,
            "fcst_lower": 119.98097832591253,
            "fcst_upper": 803.3530159354289,
        },
        {
            "time": pd.Timestamp("1962-09-01 00:00:00"),
            "fcst": 461.6670115849742,
            "fcst_lower": 111.44729181160483,
            "fcst_upper": 811.8867313583436,
        },
        {
            "time": pd.Timestamp("1962-10-01 00:00:00"),
            "fcst": 461.66700473079317,
            "fcst_lower": 103.11663171244152,
            "fcst_upper": 820.2173777491448,
        },
        {
            "time": pd.Timestamp("1962-11-01 00:00:00"),
            "fcst": 461.66700798102227,
            "fcst_lower": 94.9751928349915,
            "fcst_upper": 828.358823127053,
        },
        {
            "time": pd.Timestamp("1962-12-01 00:00:00"),
            "fcst": 461.6670064397748,
            "fcst_lower": 87.01062404537282,
            "fcst_upper": 836.3233888341767,
        },
        {
            "time": pd.Timestamp("1963-01-01 00:00:00"),
            "fcst": 461.66700717062906,
            "fcst_lower": 79.21188259460115,
            "fcst_upper": 844.122131746657,
        },
        {
            "time": pd.Timestamp("1963-02-01 00:00:00"),
            "fcst": 461.6670068240605,
            "fcst_lower": 71.56901936341609,
            "fcst_upper": 851.7649942847049,
        },
        {
            "time": pd.Timestamp("1963-03-01 00:00:00"),
            "fcst": 461.6670069884021,
            "fcst_lower": 64.07304664895531,
            "fcst_upper": 859.2609673278489,
        },
        {
            "time": pd.Timestamp("1963-04-01 00:00:00"),
            "fcst": 461.66700691047186,
            "fcst_lower": 56.715806406470165,
            "fcst_upper": 866.6182074144735,
        },
        {
            "time": pd.Timestamp("1963-05-01 00:00:00"),
            "fcst": 461.6670069474261,
            "fcst_lower": 49.48986993667626,
            "fcst_upper": 873.8441439581759,
        },
        {
            "time": pd.Timestamp("1963-06-01 00:00:00"),
            "fcst": 461.6670069299025,
            "fcst_lower": 42.388448257749985,
            "fcst_upper": 880.945565602055,
        },
    ]
)

EXOG_FCST_15_SARIMA_PARAM_EXOG_MODEL_1 = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("2020-01-18 00:00:00"),
            "fcst": 1.0082317127144957,
            "fcst_lower": 0.9923439863354956,
            "fcst_upper": 1.0241194390934958,
        },
        {
            "time": pd.Timestamp("2020-01-19 00:00:00"),
            "fcst": 0.9991862948033454,
            "fcst_lower": 0.9832141399147633,
            "fcst_upper": 1.0151584496919277,
        },
        {
            "time": pd.Timestamp("2020-01-20 00:00:00"),
            "fcst": 1.0046506773970796,
            "fcst_lower": 0.9881041550167277,
            "fcst_upper": 1.0211971997774316,
        },
        {
            "time": pd.Timestamp("2020-01-21 00:00:00"),
            "fcst": 1.0027999940466874,
            "fcst_lower": 0.9851582626466864,
            "fcst_upper": 1.0204417254466884,
        },
        {
            "time": pd.Timestamp("2020-01-22 00:00:00"),
            "fcst": 1.003219426593839,
            "fcst_lower": 0.9849304220755777,
            "fcst_upper": 1.0215084311121003,
        },
        {
            "time": pd.Timestamp("2020-01-23 00:00:00"),
            "fcst": 1.0013563760320352,
            "fcst_lower": 0.9823976131938543,
            "fcst_upper": 1.020315138870216,
        },
        {
            "time": pd.Timestamp("2020-01-24 00:00:00"),
            "fcst": 1.0029118249312734,
            "fcst_lower": 0.9832559890968382,
            "fcst_upper": 1.0225676607657086,
        },
        {
            "time": pd.Timestamp("2020-01-25 00:00:00"),
            "fcst": 0.9990584720988192,
            "fcst_lower": 0.9787586813527127,
            "fcst_upper": 1.0193582628449256,
        },
        {
            "time": pd.Timestamp("2020-01-26 00:00:00"),
            "fcst": 1.0017823097118022,
            "fcst_lower": 0.980856283700758,
            "fcst_upper": 1.0227083357228464,
        },
        {
            "time": pd.Timestamp("2020-01-27 00:00:00"),
            "fcst": 1.0001623305902951,
            "fcst_lower": 0.9786235255394861,
            "fcst_upper": 1.0217011356411043,
        },
        {
            "time": pd.Timestamp("2020-01-28 00:00:00"),
            "fcst": 1.000457094995268,
            "fcst_lower": 0.9783247990068599,
            "fcst_upper": 1.022589390983676,
        },
        {
            "time": pd.Timestamp("2020-01-29 00:00:00"),
            "fcst": 1.0000817043750918,
            "fcst_lower": 0.977371398201776,
            "fcst_upper": 1.0227920105484076,
        },
        {
            "time": pd.Timestamp("2020-01-30 00:00:00"),
            "fcst": 0.9997321013059957,
            "fcst_lower": 0.9756282277596985,
            "fcst_upper": 1.0238359748522927,
        },
        {
            "time": pd.Timestamp("2020-01-31 00:00:00"),
            "fcst": 1.003367319788623,
            "fcst_lower": 0.9786659022295303,
            "fcst_upper": 1.0280687373477158,
        },
        {
            "time": pd.Timestamp("2020-02-01 00:00:00"),
            "fcst": 1.0022894278227508,
            "fcst_lower": 0.976892260431941,
            "fcst_upper": 1.0276865952135605,
        },
    ]
)

NONSEASONAL_INPUT = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1960-12-31 00:00:00"),
            1: pd.Timestamp("1961-01-31 00:00:00"),
            2: pd.Timestamp("1961-02-28 00:00:00"),
            3: pd.Timestamp("1961-03-31 00:00:00"),
            4: pd.Timestamp("1961-04-30 00:00:00"),
            5: pd.Timestamp("1961-05-31 00:00:00"),
            6: pd.Timestamp("1961-06-30 00:00:00"),
            7: pd.Timestamp("1961-07-31 00:00:00"),
            8: pd.Timestamp("1961-08-31 00:00:00"),
            9: pd.Timestamp("1961-09-30 00:00:00"),
            10: pd.Timestamp("1961-10-31 00:00:00"),
            11: pd.Timestamp("1961-11-30 00:00:00"),
            12: pd.Timestamp("1961-12-31 00:00:00"),
            13: pd.Timestamp("1962-01-31 00:00:00"),
            14: pd.Timestamp("1962-02-28 00:00:00"),
            15: pd.Timestamp("1962-03-31 00:00:00"),
            16: pd.Timestamp("1962-04-30 00:00:00"),
            17: pd.Timestamp("1962-05-31 00:00:00"),
            18: pd.Timestamp("1962-06-30 00:00:00"),
            19: pd.Timestamp("1962-07-31 00:00:00"),
            20: pd.Timestamp("1962-08-31 00:00:00"),
            21: pd.Timestamp("1962-09-30 00:00:00"),
            22: pd.Timestamp("1962-10-31 00:00:00"),
            23: pd.Timestamp("1962-11-30 00:00:00"),
            24: pd.Timestamp("1962-12-31 00:00:00"),
        },
        "y": {
            0: 0.41506547032298174,
            1: -0.5406062106254615,
            2: 0.11083671449243261,
            3: -0.11398908479210373,
            4: 0.48307065281000106,
            5: -0.3161130468562421,
            6: 0.5990983108480854,
            7: -0.08663517357344115,
            8: -0.09999762532731679,
            9: 1.0592252850641404,
            10: -0.1660776147823171,
            11: 0.9322497147884551,
            12: 0.03697191608164456,
            13: 0.7241544636986402,
            14: -0.7964571920055838,
            15: 1.0753387472642086,
            16: 1.3483162806264335,
            17: 0.3499265428611668,
            18: -0.07003020867307755,
            19: -1.9919572119735123,
            20: 1.1230705955752662,
            21: 2.112168748216511,
            22: 0.5453426700281295,
            23: 1.3077219291558078,
            24: 1.2449380568881798,
        },
    }
)

NONSEASONAL_FUTURE_DF = pd.DataFrame(
    {
        "ds": {
            0: pd.Timestamp("1963-01-31 00:00:00"),
            1: pd.Timestamp("1963-02-28 00:00:00"),
            2: pd.Timestamp("1963-03-31 00:00:00"),
            3: pd.Timestamp("1963-04-30 00:00:00"),
            4: pd.Timestamp("1963-05-31 00:00:00"),
            5: pd.Timestamp("1963-06-30 00:00:00"),
            6: pd.Timestamp("1963-07-31 00:00:00"),
            7: pd.Timestamp("1963-08-31 00:00:00"),
            8: pd.Timestamp("1963-09-30 00:00:00"),
            9: pd.Timestamp("1963-10-31 00:00:00"),
            10: pd.Timestamp("1963-11-30 00:00:00"),
            11: pd.Timestamp("1963-12-31 00:00:00"),
        }
    }
)

AIR_FCST_30_PROPHET_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 465.59528569048166,
            1: 459.9558291481247,
            2: 492.95627065480187,
            3: 491.39646681061055,
            4: 495.8164027430265,
            5: 536.723733599239,
            6: 576.3580878717815,
            7: 576.774767410605,
            8: 528.2798791132368,
            9: 493.04059333031705,
            10: 459.2124905191395,
            11: 488.56065439007415,
            12: 501.4478393337234,
            13: 495.30135257307614,
            14: 530.862962759873,
            15: 527.178674463527,
            16: 533.1498179102681,
            17: 572.763461207553,
            18: 613.2630760453825,
            19: 613.1841652926352,
            20: 565.1767551336386,
            21: 529.5362230989792,
            22: 496.63581791637984,
            23: 526.1280789406818,
            24: 537.2652818391174,
            25: 530.6716820769692,
            26: 568.7687595327632,
            27: 562.9589993161109,
            28: 570.4916281454283,
            29: 608.7966975832746,
        },
        "fcst_lower": {
            0: 435.7227955169751,
            1: 433.03706366553985,
            2: 463.2380158942223,
            3: 461.385597691946,
            4: 467.16226356625845,
            5: 505.0131481472155,
            6: 548.5305115754014,
            7: 549.5843511864674,
            8: 502.1042013702371,
            9: 465.4473773938255,
            10: 429.7216267443718,
            11: 459.3869910121299,
            12: 472.99261225208966,
            13: 467.2334155857151,
            14: 501.96626673567454,
            15: 500.1362058155516,
            16: 504.1302881271059,
            17: 544.6241869461372,
            18: 584.0450100717637,
            19: 584.1066024817961,
            20: 537.8220302644348,
            21: 498.7658984347239,
            22: 467.7583442404464,
            23: 496.21227014845556,
            24: 510.56633711316607,
            25: 499.48092003575675,
            26: 542.4142362169579,
            27: 533.22136091424,
            28: 542.7665402452914,
            29: 581.0751908444602,
        },
        "fcst_upper": {
            0: 493.42362207487463,
            1: 487.48619890349266,
            2: 520.8302015885779,
            3: 519.7151950182429,
            4: 523.3038803967601,
            5: 565.3458444494522,
            6: 605.3689473814483,
            7: 607.5326881691888,
            8: 557.5077000916897,
            9: 522.0729932348357,
            10: 486.5037455495359,
            11: 516.9593158035708,
            12: 529.8880747286842,
            13: 523.1933917687096,
            14: 558.6142240080726,
            15: 556.5583959018988,
            16: 559.6401054062931,
            17: 601.5975821122233,
            18: 641.4213271589108,
            19: 642.9538881677378,
            20: 593.1390965161879,
            21: 559.2586524345785,
            22: 525.82443333545,
            23: 554.3688117496768,
            24: 564.2766083727195,
            25: 559.1708161195501,
            26: 598.218095935576,
            27: 591.131489011433,
            28: 601.7309712860445,
            29: 637.9083803171933,
        },
    }
)

AIR_FCST_30_PROPHET_CAP_AND_FLOOR_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 475.1575442103092,
            1: 469.24179608421866,
            2: 503.11659609624536,
            3: 502.31359340950956,
            4: 506.85396492553156,
            5: 548.6845145501933,
            6: 588.6703496295718,
            7: 589.3272437420154,
            8: 541.0900404153592,
            9: 507.00020458085845,
            10: 473.2248193157536,
            11: 503.0391202867464,
            12: 516.0508016418704,
            13: 510.23415966933925,
            14: 546.491165453043,
            15: 542.9398772109357,
            16: 549.8053457737586,
            17: 589.4433541284543,
            18: 630.409646625312,
            19: 630.9017169479421,
            20: 583.9416416493654,
            21: 547.92435041299,
            22: 515.6912215970992,
            23: 545.1485026034558,
            24: 556.4324168509892,
            25: 550.7215796751234,
            26: 589.2719067529652,
            27: 582.9292377569677,
            28: 592.0798841970798,
            29: 629.4582350097522,
        },
        "fcst_lower": {
            0: 445.3418598594581,
            1: 442.37954981156753,
            2: 473.4261904547741,
            3: 472.3495518433545,
            4: 478.24327458785837,
            5: 517.0701518389475,
            6: 560.8025261752462,
            7: 562.294897734461,
            8: 514.9341473365542,
            9: 479.45553157412814,
            10: 443.9014610568383,
            11: 473.9696193939736,
            12: 487.52992050249156,
            13: 481.98817396338916,
            14: 516.8915086392954,
            15: 515.3912239207851,
            16: 520.840187606289,
            17: 560.923719628996,
            18: 601.2325344593912,
            19: 601.8209664312715,
            20: 556.6303990916655,
            21: 517.44076619717,
            22: 486.8662571363469,
            23: 515.3865991708863,
            24: 529.6661865746032,
            25: 519.649083216983,
            26: 563.0662280239728,
            27: 552.6646271671854,
            28: 564.7986165777385,
            29: 601.7352512977587,
        },
        "fcst_upper": {
            0: 502.93296195510214,
            1: 496.7902750763139,
            2: 531.0465021126715,
            3: 530.5185358828127,
            4: 534.2851110611824,
            5: 577.5091588015033,
            6: 617.6355544964309,
            7: 620.0970379564785,
            8: 570.3684024348204,
            9: 536.0907703966614,
            10: 500.19801938833615,
            11: 531.3327035010188,
            12: 544.6015513834924,
            13: 537.7868775369062,
            14: 573.8837866856328,
            15: 572.0145294514786,
            16: 575.841124061249,
            17: 617.9573369540493,
            18: 658.7007736374042,
            19: 660.742916185678,
            20: 611.7835417415275,
            21: 577.5572482204245,
            22: 544.3169813165081,
            23: 573.2953511012179,
            24: 583.6627573206079,
            25: 578.8415342181587,
            26: 618.0269834406183,
            27: 610.4770092899499,
            28: 623.1408929198517,
            29: 657.7775827360285,
        },
    }
)

PEYTON_FCST_30_PROPHET_CAP_AND_FLOOR_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 9.86101525181464,
            1: 9.89003240961042,
            2: 9.930278231638619,
            3: 9.784139043582456,
            4: 10.181742594673318,
            5: 10.3023632182167,
            6: 10.050429250465271,
            7: 9.861015251814367,
            8: 9.890032409610013,
            9: 9.930278231638434,
            10: 9.784139043582357,
            11: 10.181742594672786,
            12: 10.302363218216716,
            13: 10.050429250464921,
            14: 9.861015251814095,
            15: 9.890032409610109,
            16: 9.930278231638638,
            17: 9.784139043582408,
            18: 10.181742594673226,
            19: 10.302363218216732,
            20: 10.050429250464944,
            21: 9.861015251814528,
            22: 9.890032409610107,
            23: 9.93027823163856,
            24: 9.78413904358252,
            25: 10.18174259467248,
            26: 10.302363218216582,
            27: 10.050429250464951,
            28: 9.861015251814257,
            29: 9.890032409610107,
        },
        "fcst_lower": {
            0: 7.523186110258121,
            1: 7.575393432516099,
            2: 7.6355617294543,
            3: 7.6252084389987855,
            4: 7.9639238986736665,
            5: 7.876072621875117,
            6: 7.953465126288597,
            7: 7.652371536106707,
            8: 7.619598524085517,
            9: 7.727352392038587,
            10: 7.590527949007888,
            11: 8.023666367393657,
            12: 8.020968141056638,
            13: 7.747017837209666,
            14: 7.629569241285518,
            15: 7.83573566653282,
            16: 7.870761785182868,
            17: 7.392173418278055,
            18: 8.072712388162977,
            19: 8.164211695364877,
            20: 7.753943792351764,
            21: 7.6251760230290575,
            22: 7.639626386920325,
            23: 7.6301289075174115,
            24: 7.641139952361675,
            25: 8.028837974985159,
            26: 8.01673544100944,
            27: 7.683822540708778,
            28: 7.561195117618204,
            29: 7.99594817226005,
        },
        "fcst_upper": {
            0: 12.089732898097001,
            1: 11.984250766806364,
            2: 12.19042330428994,
            3: 12.153367218555255,
            4: 12.487552941138016,
            5: 12.479388409934085,
            6: 12.237686150431063,
            7: 12.06206958905758,
            8: 11.96067710177131,
            9: 12.055337761921143,
            10: 12.037647958894585,
            11: 12.282405332374646,
            12: 12.29542065922854,
            13: 12.206035177309621,
            14: 12.103815485014408,
            15: 12.035622751632467,
            16: 12.198735175562952,
            17: 11.922095489765741,
            18: 12.383072433918803,
            19: 12.435352494215513,
            20: 12.147049325915042,
            21: 11.962957076996277,
            22: 12.081392901358322,
            23: 12.175395109201133,
            24: 11.998325143455052,
            25: 12.441980195057987,
            26: 12.502881352584524,
            27: 12.281079278077286,
            28: 12.01665981197022,
            29: 12.021690364457857,
        },
    }
)

AIR_FCST_30_PROPHET_INCL_HIST_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1949-01-01 00:00:00"),
            1: pd.Timestamp("1949-02-01 00:00:00"),
            2: pd.Timestamp("1949-03-01 00:00:00"),
            3: pd.Timestamp("1949-04-01 00:00:00"),
            4: pd.Timestamp("1949-05-01 00:00:00"),
            5: pd.Timestamp("1949-06-01 00:00:00"),
            6: pd.Timestamp("1949-07-01 00:00:00"),
            7: pd.Timestamp("1949-08-01 00:00:00"),
            8: pd.Timestamp("1949-09-01 00:00:00"),
            9: pd.Timestamp("1949-10-01 00:00:00"),
            10: pd.Timestamp("1949-11-01 00:00:00"),
            11: pd.Timestamp("1949-12-01 00:00:00"),
            12: pd.Timestamp("1950-01-01 00:00:00"),
            13: pd.Timestamp("1950-02-01 00:00:00"),
            14: pd.Timestamp("1950-03-01 00:00:00"),
            15: pd.Timestamp("1950-04-01 00:00:00"),
            16: pd.Timestamp("1950-05-01 00:00:00"),
            17: pd.Timestamp("1950-06-01 00:00:00"),
            18: pd.Timestamp("1950-07-01 00:00:00"),
            19: pd.Timestamp("1950-08-01 00:00:00"),
            20: pd.Timestamp("1950-09-01 00:00:00"),
            21: pd.Timestamp("1950-10-01 00:00:00"),
            22: pd.Timestamp("1950-11-01 00:00:00"),
            23: pd.Timestamp("1950-12-01 00:00:00"),
            24: pd.Timestamp("1951-01-01 00:00:00"),
            25: pd.Timestamp("1951-02-01 00:00:00"),
            26: pd.Timestamp("1951-03-01 00:00:00"),
            27: pd.Timestamp("1951-04-01 00:00:00"),
            28: pd.Timestamp("1951-05-01 00:00:00"),
            29: pd.Timestamp("1951-06-01 00:00:00"),
            30: pd.Timestamp("1951-07-01 00:00:00"),
            31: pd.Timestamp("1951-08-01 00:00:00"),
            32: pd.Timestamp("1951-09-01 00:00:00"),
            33: pd.Timestamp("1951-10-01 00:00:00"),
            34: pd.Timestamp("1951-11-01 00:00:00"),
            35: pd.Timestamp("1951-12-01 00:00:00"),
            36: pd.Timestamp("1952-01-01 00:00:00"),
            37: pd.Timestamp("1952-02-01 00:00:00"),
            38: pd.Timestamp("1952-03-01 00:00:00"),
            39: pd.Timestamp("1952-04-01 00:00:00"),
            40: pd.Timestamp("1952-05-01 00:00:00"),
            41: pd.Timestamp("1952-06-01 00:00:00"),
            42: pd.Timestamp("1952-07-01 00:00:00"),
            43: pd.Timestamp("1952-08-01 00:00:00"),
            44: pd.Timestamp("1952-09-01 00:00:00"),
            45: pd.Timestamp("1952-10-01 00:00:00"),
            46: pd.Timestamp("1952-11-01 00:00:00"),
            47: pd.Timestamp("1952-12-01 00:00:00"),
            48: pd.Timestamp("1953-01-01 00:00:00"),
            49: pd.Timestamp("1953-02-01 00:00:00"),
            50: pd.Timestamp("1953-03-01 00:00:00"),
            51: pd.Timestamp("1953-04-01 00:00:00"),
            52: pd.Timestamp("1953-05-01 00:00:00"),
            53: pd.Timestamp("1953-06-01 00:00:00"),
            54: pd.Timestamp("1953-07-01 00:00:00"),
            55: pd.Timestamp("1953-08-01 00:00:00"),
            56: pd.Timestamp("1953-09-01 00:00:00"),
            57: pd.Timestamp("1953-10-01 00:00:00"),
            58: pd.Timestamp("1953-11-01 00:00:00"),
            59: pd.Timestamp("1953-12-01 00:00:00"),
            60: pd.Timestamp("1954-01-01 00:00:00"),
            61: pd.Timestamp("1954-02-01 00:00:00"),
            62: pd.Timestamp("1954-03-01 00:00:00"),
            63: pd.Timestamp("1954-04-01 00:00:00"),
            64: pd.Timestamp("1954-05-01 00:00:00"),
            65: pd.Timestamp("1954-06-01 00:00:00"),
            66: pd.Timestamp("1954-07-01 00:00:00"),
            67: pd.Timestamp("1954-08-01 00:00:00"),
            68: pd.Timestamp("1954-09-01 00:00:00"),
            69: pd.Timestamp("1954-10-01 00:00:00"),
            70: pd.Timestamp("1954-11-01 00:00:00"),
            71: pd.Timestamp("1954-12-01 00:00:00"),
            72: pd.Timestamp("1955-01-01 00:00:00"),
            73: pd.Timestamp("1955-02-01 00:00:00"),
            74: pd.Timestamp("1955-03-01 00:00:00"),
            75: pd.Timestamp("1955-04-01 00:00:00"),
            76: pd.Timestamp("1955-05-01 00:00:00"),
            77: pd.Timestamp("1955-06-01 00:00:00"),
            78: pd.Timestamp("1955-07-01 00:00:00"),
            79: pd.Timestamp("1955-08-01 00:00:00"),
            80: pd.Timestamp("1955-09-01 00:00:00"),
            81: pd.Timestamp("1955-10-01 00:00:00"),
            82: pd.Timestamp("1955-11-01 00:00:00"),
            83: pd.Timestamp("1955-12-01 00:00:00"),
            84: pd.Timestamp("1956-01-01 00:00:00"),
            85: pd.Timestamp("1956-02-01 00:00:00"),
            86: pd.Timestamp("1956-03-01 00:00:00"),
            87: pd.Timestamp("1956-04-01 00:00:00"),
            88: pd.Timestamp("1956-05-01 00:00:00"),
            89: pd.Timestamp("1956-06-01 00:00:00"),
            90: pd.Timestamp("1956-07-01 00:00:00"),
            91: pd.Timestamp("1956-08-01 00:00:00"),
            92: pd.Timestamp("1956-09-01 00:00:00"),
            93: pd.Timestamp("1956-10-01 00:00:00"),
            94: pd.Timestamp("1956-11-01 00:00:00"),
            95: pd.Timestamp("1956-12-01 00:00:00"),
            96: pd.Timestamp("1957-01-01 00:00:00"),
            97: pd.Timestamp("1957-02-01 00:00:00"),
            98: pd.Timestamp("1957-03-01 00:00:00"),
            99: pd.Timestamp("1957-04-01 00:00:00"),
            100: pd.Timestamp("1957-05-01 00:00:00"),
            101: pd.Timestamp("1957-06-01 00:00:00"),
            102: pd.Timestamp("1957-07-01 00:00:00"),
            103: pd.Timestamp("1957-08-01 00:00:00"),
            104: pd.Timestamp("1957-09-01 00:00:00"),
            105: pd.Timestamp("1957-10-01 00:00:00"),
            106: pd.Timestamp("1957-11-01 00:00:00"),
            107: pd.Timestamp("1957-12-01 00:00:00"),
            108: pd.Timestamp("1958-01-01 00:00:00"),
            109: pd.Timestamp("1958-02-01 00:00:00"),
            110: pd.Timestamp("1958-03-01 00:00:00"),
            111: pd.Timestamp("1958-04-01 00:00:00"),
            112: pd.Timestamp("1958-05-01 00:00:00"),
            113: pd.Timestamp("1958-06-01 00:00:00"),
            114: pd.Timestamp("1958-07-01 00:00:00"),
            115: pd.Timestamp("1958-08-01 00:00:00"),
            116: pd.Timestamp("1958-09-01 00:00:00"),
            117: pd.Timestamp("1958-10-01 00:00:00"),
            118: pd.Timestamp("1958-11-01 00:00:00"),
            119: pd.Timestamp("1958-12-01 00:00:00"),
            120: pd.Timestamp("1959-01-01 00:00:00"),
            121: pd.Timestamp("1959-02-01 00:00:00"),
            122: pd.Timestamp("1959-03-01 00:00:00"),
            123: pd.Timestamp("1959-04-01 00:00:00"),
            124: pd.Timestamp("1959-05-01 00:00:00"),
            125: pd.Timestamp("1959-06-01 00:00:00"),
            126: pd.Timestamp("1959-07-01 00:00:00"),
            127: pd.Timestamp("1959-08-01 00:00:00"),
            128: pd.Timestamp("1959-09-01 00:00:00"),
            129: pd.Timestamp("1959-10-01 00:00:00"),
            130: pd.Timestamp("1959-11-01 00:00:00"),
            131: pd.Timestamp("1959-12-01 00:00:00"),
            132: pd.Timestamp("1960-01-01 00:00:00"),
            133: pd.Timestamp("1960-02-01 00:00:00"),
            134: pd.Timestamp("1960-03-01 00:00:00"),
            135: pd.Timestamp("1960-04-01 00:00:00"),
            136: pd.Timestamp("1960-05-01 00:00:00"),
            137: pd.Timestamp("1960-06-01 00:00:00"),
            138: pd.Timestamp("1960-07-01 00:00:00"),
            139: pd.Timestamp("1960-08-01 00:00:00"),
            140: pd.Timestamp("1960-09-01 00:00:00"),
            141: pd.Timestamp("1960-10-01 00:00:00"),
            142: pd.Timestamp("1960-11-01 00:00:00"),
            143: pd.Timestamp("1960-12-01 00:00:00"),
            144: pd.Timestamp("1961-01-01 00:00:00"),
            145: pd.Timestamp("1961-02-01 00:00:00"),
            146: pd.Timestamp("1961-03-01 00:00:00"),
            147: pd.Timestamp("1961-04-01 00:00:00"),
            148: pd.Timestamp("1961-05-01 00:00:00"),
            149: pd.Timestamp("1961-06-01 00:00:00"),
            150: pd.Timestamp("1961-07-01 00:00:00"),
            151: pd.Timestamp("1961-08-01 00:00:00"),
            152: pd.Timestamp("1961-09-01 00:00:00"),
            153: pd.Timestamp("1961-10-01 00:00:00"),
            154: pd.Timestamp("1961-11-01 00:00:00"),
            155: pd.Timestamp("1961-12-01 00:00:00"),
            156: pd.Timestamp("1962-01-01 00:00:00"),
            157: pd.Timestamp("1962-02-01 00:00:00"),
            158: pd.Timestamp("1962-03-01 00:00:00"),
            159: pd.Timestamp("1962-04-01 00:00:00"),
            160: pd.Timestamp("1962-05-01 00:00:00"),
            161: pd.Timestamp("1962-06-01 00:00:00"),
            162: pd.Timestamp("1962-07-01 00:00:00"),
            163: pd.Timestamp("1962-08-01 00:00:00"),
            164: pd.Timestamp("1962-09-01 00:00:00"),
            165: pd.Timestamp("1962-10-01 00:00:00"),
            166: pd.Timestamp("1962-11-01 00:00:00"),
            167: pd.Timestamp("1962-12-01 00:00:00"),
            168: pd.Timestamp("1963-01-01 00:00:00"),
            169: pd.Timestamp("1963-02-01 00:00:00"),
            170: pd.Timestamp("1963-03-01 00:00:00"),
            171: pd.Timestamp("1963-04-01 00:00:00"),
            172: pd.Timestamp("1963-05-01 00:00:00"),
            173: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 84.82713996622525,
            1: 78.24556990885091,
            2: 110.39507017615126,
            3: 107.89315281694103,
            4: 111.40136599288164,
            5: 151.36658333407547,
            6: 190.08921485228058,
            7: 189.56378087828537,
            8: 140.12677906809756,
            9: 103.97577053084026,
            10: 69.20555419659107,
            11: 97.64199530326354,
            12: 109.58706672384726,
            13: 102.49846644013039,
            14: 137.20913538027463,
            15: 132.5827335492101,
            16: 137.64215422040246,
            17: 176.31368398296644,
            18: 215.90157604524956,
            19: 214.88055174751753,
            20: 165.93102804353725,
            21: 129.37877322340452,
            22: 95.53625449581821,
            23: 124.11679273464871,
            24: 134.311882087584,
            25: 126.7761687799386,
            26: 164.0223049688268,
            27: 157.27043120667446,
            28: 163.8913372675921,
            29: 201.25429317809787,
            30: 241.7018989572827,
            31: 240.18370293200667,
            32: 191.7408539458812,
            33: 154.8000112054635,
            34: 121.89410719468887,
            35: 150.5615090229799,
            36: 159.0026457389163,
            37: 151.080063697262,
            38: 185.96938277248597,
            39: 185.58930116153655,
            40: 187.5578075240549,
            41: 228.8011362549425,
            42: 266.6531983389425,
            43: 266.6209301528497,
            44: 216.71573404290558,
            45: 180.97873028498896,
            46: 145.2911803543033,
            47: 173.52648225449315,
            48: 187.21527988380893,
            49: 180.63371150687743,
            50: 212.78321329201103,
            51: 210.2813905574789,
            52: 213.78969530571166,
            53: 253.75500727159042,
            54: 292.47773035993964,
            55: 291.95239100842906,
            56: 242.63914194729588,
            57: 206.60789413492213,
            58: 171.96143055997842,
            59: 200.5176324014636,
            60: 212.77349209801784,
            61: 205.99568009027914,
            62: 240.9870610216413,
            63: 236.67144747820177,
            64: 242.0316310084053,
            65: 281.2558273795679,
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        "fcst_lower": {
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        "fcst_upper": {
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        },
    }
)

PEYTON_FCST_15_PROPHET_INCL_HIST_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2012-05-02 00:00:00"),
            1: pd.Timestamp("2012-05-03 00:00:00"),
            2: pd.Timestamp("2012-05-04 00:00:00"),
            3: pd.Timestamp("2012-05-05 00:00:00"),
            4: pd.Timestamp("2012-05-06 00:00:00"),
            5: pd.Timestamp("2012-05-07 00:00:00"),
            6: pd.Timestamp("2012-05-08 00:00:00"),
            7: pd.Timestamp("2012-05-09 00:00:00"),
            8: pd.Timestamp("2012-05-10 00:00:00"),
            9: pd.Timestamp("2012-05-11 00:00:00"),
            10: pd.Timestamp("2012-05-12 00:00:00"),
            11: pd.Timestamp("2012-05-13 00:00:00"),
            12: pd.Timestamp("2012-05-14 00:00:00"),
            13: pd.Timestamp("2012-05-15 00:00:00"),
            14: pd.Timestamp("2012-05-16 00:00:00"),
            15: pd.Timestamp("2012-05-17 00:00:00"),
            16: pd.Timestamp("2012-05-18 00:00:00"),
            17: pd.Timestamp("2012-05-19 00:00:00"),
            18: pd.Timestamp("2012-05-20 00:00:00"),
            19: pd.Timestamp("2012-05-21 00:00:00"),
            20: pd.Timestamp("2012-05-22 00:00:00"),
            21: pd.Timestamp("2012-05-23 00:00:00"),
            22: pd.Timestamp("2012-05-24 00:00:00"),
            23: pd.Timestamp("2012-05-25 00:00:00"),
            24: pd.Timestamp("2012-05-26 00:00:00"),
            25: pd.Timestamp("2012-05-27 00:00:00"),
            26: pd.Timestamp("2012-05-28 00:00:00"),
            27: pd.Timestamp("2012-05-29 00:00:00"),
            28: pd.Timestamp("2012-05-30 00:00:00"),
            29: pd.Timestamp("2012-05-31 00:00:00"),
            30: pd.Timestamp("2012-06-01 00:00:00"),
            31: pd.Timestamp("2012-06-02 00:00:00"),
            32: pd.Timestamp("2012-06-03 00:00:00"),
            33: pd.Timestamp("2012-06-04 00:00:00"),
            34: pd.Timestamp("2012-06-05 00:00:00"),
            35: pd.Timestamp("2012-06-06 00:00:00"),
            36: pd.Timestamp("2012-06-07 00:00:00"),
            37: pd.Timestamp("2012-06-08 00:00:00"),
            38: pd.Timestamp("2012-06-09 00:00:00"),
            39: pd.Timestamp("2012-06-10 00:00:00"),
            40: pd.Timestamp("2012-06-11 00:00:00"),
            41: pd.Timestamp("2012-06-12 00:00:00"),
            42: pd.Timestamp("2012-06-13 00:00:00"),
            43: pd.Timestamp("2012-06-14 00:00:00"),
            44: pd.Timestamp("2012-06-15 00:00:00"),
            45: pd.Timestamp("2012-06-16 00:00:00"),
            46: pd.Timestamp("2012-06-17 00:00:00"),
            47: pd.Timestamp("2012-06-18 00:00:00"),
            48: pd.Timestamp("2012-06-19 00:00:00"),
            49: pd.Timestamp("2012-06-20 00:00:00"),
            50: pd.Timestamp("2012-06-21 00:00:00"),
            51: pd.Timestamp("2012-06-22 00:00:00"),
            52: pd.Timestamp("2012-06-23 00:00:00"),
            53: pd.Timestamp("2012-06-24 00:00:00"),
            54: pd.Timestamp("2012-06-25 00:00:00"),
            55: pd.Timestamp("2012-06-26 00:00:00"),
            56: pd.Timestamp("2012-06-27 00:00:00"),
            57: pd.Timestamp("2012-06-28 00:00:00"),
            58: pd.Timestamp("2012-06-29 00:00:00"),
            59: pd.Timestamp("2012-06-30 00:00:00"),
            60: pd.Timestamp("2012-07-01 00:00:00"),
            61: pd.Timestamp("2012-07-02 00:00:00"),
            62: pd.Timestamp("2012-07-03 00:00:00"),
            63: pd.Timestamp("2012-07-04 00:00:00"),
            64: pd.Timestamp("2012-07-05 00:00:00"),
            65: pd.Timestamp("2012-07-06 00:00:00"),
            66: pd.Timestamp("2012-07-07 00:00:00"),
            67: pd.Timestamp("2012-07-08 00:00:00"),
            68: pd.Timestamp("2012-07-09 00:00:00"),
            69: pd.Timestamp("2012-07-10 00:00:00"),
            70: pd.Timestamp("2012-07-11 00:00:00"),
            71: pd.Timestamp("2012-07-12 00:00:00"),
            72: pd.Timestamp("2012-07-13 00:00:00"),
            73: pd.Timestamp("2012-07-14 00:00:00"),
            74: pd.Timestamp("2012-07-15 00:00:00"),
            75: pd.Timestamp("2012-07-16 00:00:00"),
            76: pd.Timestamp("2012-07-17 00:00:00"),
            77: pd.Timestamp("2012-07-18 00:00:00"),
            78: pd.Timestamp("2012-07-19 00:00:00"),
            79: pd.Timestamp("2012-07-20 00:00:00"),
            80: pd.Timestamp("2012-07-21 00:00:00"),
            81: pd.Timestamp("2012-07-22 00:00:00"),
            82: pd.Timestamp("2012-07-23 00:00:00"),
            83: pd.Timestamp("2012-07-24 00:00:00"),
            84: pd.Timestamp("2012-07-25 00:00:00"),
            85: pd.Timestamp("2012-07-26 00:00:00"),
            86: pd.Timestamp("2012-07-27 00:00:00"),
            87: pd.Timestamp("2012-07-28 00:00:00"),
            88: pd.Timestamp("2012-07-29 00:00:00"),
            89: pd.Timestamp("2012-07-30 00:00:00"),
            90: pd.Timestamp("2012-07-31 00:00:00"),
            91: pd.Timestamp("2012-08-01 00:00:00"),
            92: pd.Timestamp("2012-08-02 00:00:00"),
            93: pd.Timestamp("2012-08-03 00:00:00"),
            94: pd.Timestamp("2012-08-04 00:00:00"),
            95: pd.Timestamp("2012-08-05 00:00:00"),
            96: pd.Timestamp("2012-08-06 00:00:00"),
            97: pd.Timestamp("2012-08-07 00:00:00"),
            98: pd.Timestamp("2012-08-08 00:00:00"),
            99: pd.Timestamp("2012-08-09 00:00:00"),
            100: pd.Timestamp("2012-08-10 00:00:00"),
            101: pd.Timestamp("2012-08-11 00:00:00"),
            102: pd.Timestamp("2012-08-12 00:00:00"),
            103: pd.Timestamp("2012-08-13 00:00:00"),
            104: pd.Timestamp("2012-08-14 00:00:00"),
            105: pd.Timestamp("2012-08-15 00:00:00"),
            106: pd.Timestamp("2012-08-16 00:00:00"),
            107: pd.Timestamp("2012-08-17 00:00:00"),
            108: pd.Timestamp("2012-08-18 00:00:00"),
            109: pd.Timestamp("2012-08-19 00:00:00"),
            110: pd.Timestamp("2012-08-20 00:00:00"),
            111: pd.Timestamp("2012-08-21 00:00:00"),
            112: pd.Timestamp("2012-08-22 00:00:00"),
            113: pd.Timestamp("2012-08-23 00:00:00"),
            114: pd.Timestamp("2012-08-24 00:00:00"),
            115: pd.Timestamp("2012-08-25 00:00:00"),
            116: pd.Timestamp("2012-08-26 00:00:00"),
            117: pd.Timestamp("2012-08-27 00:00:00"),
            118: pd.Timestamp("2012-08-28 00:00:00"),
            119: pd.Timestamp("2012-08-29 00:00:00"),
            120: pd.Timestamp("2012-08-30 00:00:00"),
            121: pd.Timestamp("2012-08-31 00:00:00"),
            122: pd.Timestamp("2012-09-01 00:00:00"),
            123: pd.Timestamp("2012-09-02 00:00:00"),
            124: pd.Timestamp("2012-09-03 00:00:00"),
            125: pd.Timestamp("2012-09-04 00:00:00"),
            126: pd.Timestamp("2012-09-05 00:00:00"),
            127: pd.Timestamp("2012-09-06 00:00:00"),
            128: pd.Timestamp("2012-09-07 00:00:00"),
            129: pd.Timestamp("2012-09-08 00:00:00"),
            130: pd.Timestamp("2012-09-09 00:00:00"),
            131: pd.Timestamp("2012-09-10 00:00:00"),
            132: pd.Timestamp("2012-09-11 00:00:00"),
            133: pd.Timestamp("2012-09-12 00:00:00"),
            134: pd.Timestamp("2012-09-13 00:00:00"),
            135: pd.Timestamp("2012-09-14 00:00:00"),
            136: pd.Timestamp("2012-09-15 00:00:00"),
            137: pd.Timestamp("2012-09-16 00:00:00"),
            138: pd.Timestamp("2012-09-17 00:00:00"),
            139: pd.Timestamp("2012-09-18 00:00:00"),
            140: pd.Timestamp("2012-09-19 00:00:00"),
            141: pd.Timestamp("2012-09-20 00:00:00"),
            142: pd.Timestamp("2012-09-21 00:00:00"),
            143: pd.Timestamp("2012-09-22 00:00:00"),
            144: pd.Timestamp("2012-09-23 00:00:00"),
            145: pd.Timestamp("2012-09-24 00:00:00"),
            146: pd.Timestamp("2012-09-25 00:00:00"),
            147: pd.Timestamp("2012-09-26 00:00:00"),
            148: pd.Timestamp("2012-09-27 00:00:00"),
            149: pd.Timestamp("2012-09-28 00:00:00"),
            150: pd.Timestamp("2012-09-29 00:00:00"),
            151: pd.Timestamp("2012-09-30 00:00:00"),
            152: pd.Timestamp("2012-10-01 00:00:00"),
            153: pd.Timestamp("2012-10-02 00:00:00"),
            154: pd.Timestamp("2012-10-03 00:00:00"),
            155: pd.Timestamp("2012-10-04 00:00:00"),
            156: pd.Timestamp("2012-10-05 00:00:00"),
            157: pd.Timestamp("2012-10-06 00:00:00"),
            158: pd.Timestamp("2012-10-07 00:00:00"),
            159: pd.Timestamp("2012-10-08 00:00:00"),
            160: pd.Timestamp("2012-10-09 00:00:00"),
            161: pd.Timestamp("2012-10-10 00:00:00"),
            162: pd.Timestamp("2012-10-11 00:00:00"),
            163: pd.Timestamp("2012-10-12 00:00:00"),
            164: pd.Timestamp("2012-10-13 00:00:00"),
            165: pd.Timestamp("2012-10-14 00:00:00"),
            166: pd.Timestamp("2012-10-15 00:00:00"),
            167: pd.Timestamp("2012-10-16 00:00:00"),
            168: pd.Timestamp("2012-10-17 00:00:00"),
            169: pd.Timestamp("2012-10-18 00:00:00"),
            170: pd.Timestamp("2012-10-19 00:00:00"),
            171: pd.Timestamp("2012-10-20 00:00:00"),
            172: pd.Timestamp("2012-10-21 00:00:00"),
            173: pd.Timestamp("2012-10-22 00:00:00"),
            174: pd.Timestamp("2012-10-23 00:00:00"),
            175: pd.Timestamp("2012-10-24 00:00:00"),
            176: pd.Timestamp("2012-10-25 00:00:00"),
            177: pd.Timestamp("2012-10-26 00:00:00"),
            178: pd.Timestamp("2012-10-27 00:00:00"),
            179: pd.Timestamp("2012-10-28 00:00:00"),
            180: pd.Timestamp("2012-10-29 00:00:00"),
            181: pd.Timestamp("2012-10-30 00:00:00"),
            182: pd.Timestamp("2012-10-31 00:00:00"),
            183: pd.Timestamp("2012-11-01 00:00:00"),
            184: pd.Timestamp("2012-11-02 00:00:00"),
            185: pd.Timestamp("2012-11-03 00:00:00"),
            186: pd.Timestamp("2012-11-04 00:00:00"),
            187: pd.Timestamp("2012-11-05 00:00:00"),
            188: pd.Timestamp("2012-11-06 00:00:00"),
            189: pd.Timestamp("2012-11-07 00:00:00"),
            190: pd.Timestamp("2012-11-08 00:00:00"),
            191: pd.Timestamp("2012-11-09 00:00:00"),
            192: pd.Timestamp("2012-11-10 00:00:00"),
            193: pd.Timestamp("2012-11-11 00:00:00"),
            194: pd.Timestamp("2012-11-12 00:00:00"),
            195: pd.Timestamp("2012-11-13 00:00:00"),
            196: pd.Timestamp("2012-11-14 00:00:00"),
            197: pd.Timestamp("2012-11-15 00:00:00"),
            198: pd.Timestamp("2012-11-16 00:00:00"),
            199: pd.Timestamp("2012-11-17 00:00:00"),
            200: pd.Timestamp("2012-11-18 00:00:00"),
            201: pd.Timestamp("2012-11-19 00:00:00"),
            202: pd.Timestamp("2012-11-20 00:00:00"),
            203: pd.Timestamp("2012-11-21 00:00:00"),
            204: pd.Timestamp("2012-11-22 00:00:00"),
            205: pd.Timestamp("2012-11-23 00:00:00"),
            206: pd.Timestamp("2012-11-24 00:00:00"),
            207: pd.Timestamp("2012-11-25 00:00:00"),
            208: pd.Timestamp("2012-11-26 00:00:00"),
            209: pd.Timestamp("2012-11-27 00:00:00"),
            210: pd.Timestamp("2012-11-28 00:00:00"),
            211: pd.Timestamp("2012-11-29 00:00:00"),
            212: pd.Timestamp("2012-11-30 00:00:00"),
            213: pd.Timestamp("2012-12-01 00:00:00"),
            214: pd.Timestamp("2012-12-02 00:00:00"),
            215: pd.Timestamp("2012-12-03 00:00:00"),
            216: pd.Timestamp("2012-12-04 00:00:00"),
            217: pd.Timestamp("2012-12-05 00:00:00"),
            218: pd.Timestamp("2012-12-06 00:00:00"),
            219: pd.Timestamp("2012-12-07 00:00:00"),
            220: pd.Timestamp("2012-12-08 00:00:00"),
            221: pd.Timestamp("2012-12-09 00:00:00"),
            222: pd.Timestamp("2012-12-10 00:00:00"),
            223: pd.Timestamp("2012-12-11 00:00:00"),
            224: pd.Timestamp("2012-12-12 00:00:00"),
            225: pd.Timestamp("2012-12-13 00:00:00"),
            226: pd.Timestamp("2012-12-14 00:00:00"),
            227: pd.Timestamp("2012-12-15 00:00:00"),
            228: pd.Timestamp("2012-12-16 00:00:00"),
            229: pd.Timestamp("2012-12-17 00:00:00"),
            230: pd.Timestamp("2012-12-18 00:00:00"),
            231: pd.Timestamp("2012-12-19 00:00:00"),
            232: pd.Timestamp("2012-12-20 00:00:00"),
            233: pd.Timestamp("2012-12-21 00:00:00"),
            234: pd.Timestamp("2012-12-22 00:00:00"),
            235: pd.Timestamp("2012-12-23 00:00:00"),
            236: pd.Timestamp("2012-12-24 00:00:00"),
            237: pd.Timestamp("2012-12-25 00:00:00"),
            238: pd.Timestamp("2012-12-26 00:00:00"),
            239: pd.Timestamp("2012-12-27 00:00:00"),
            240: pd.Timestamp("2012-12-28 00:00:00"),
            241: pd.Timestamp("2012-12-29 00:00:00"),
            242: pd.Timestamp("2012-12-30 00:00:00"),
            243: pd.Timestamp("2012-12-31 00:00:00"),
            244: pd.Timestamp("2013-01-01 00:00:00"),
            245: pd.Timestamp("2013-01-02 00:00:00"),
            246: pd.Timestamp("2013-01-03 00:00:00"),
            247: pd.Timestamp("2013-01-04 00:00:00"),
            248: pd.Timestamp("2013-01-05 00:00:00"),
            249: pd.Timestamp("2013-01-06 00:00:00"),
            250: pd.Timestamp("2013-01-07 00:00:00"),
            251: pd.Timestamp("2013-01-08 00:00:00"),
            252: pd.Timestamp("2013-01-09 00:00:00"),
            253: pd.Timestamp("2013-01-10 00:00:00"),
            254: pd.Timestamp("2013-01-11 00:00:00"),
            255: pd.Timestamp("2013-01-12 00:00:00"),
            256: pd.Timestamp("2013-01-13 00:00:00"),
            257: pd.Timestamp("2013-01-14 00:00:00"),
            258: pd.Timestamp("2013-01-15 00:00:00"),
            259: pd.Timestamp("2013-01-16 00:00:00"),
            260: pd.Timestamp("2013-01-17 00:00:00"),
            261: pd.Timestamp("2013-01-18 00:00:00"),
            262: pd.Timestamp("2013-01-19 00:00:00"),
            263: pd.Timestamp("2013-01-20 00:00:00"),
            264: pd.Timestamp("2013-01-21 00:00:00"),
            265: pd.Timestamp("2013-01-22 00:00:00"),
            266: pd.Timestamp("2013-01-23 00:00:00"),
            267: pd.Timestamp("2013-01-24 00:00:00"),
            268: pd.Timestamp("2013-01-25 00:00:00"),
            269: pd.Timestamp("2013-01-26 00:00:00"),
            270: pd.Timestamp("2013-01-27 00:00:00"),
            271: pd.Timestamp("2013-01-28 00:00:00"),
            272: pd.Timestamp("2013-01-29 00:00:00"),
            273: pd.Timestamp("2013-01-30 00:00:00"),
            274: pd.Timestamp("2013-01-31 00:00:00"),
            275: pd.Timestamp("2013-02-01 00:00:00"),
            276: pd.Timestamp("2013-02-02 00:00:00"),
            277: pd.Timestamp("2013-02-03 00:00:00"),
            278: pd.Timestamp("2013-02-04 00:00:00"),
            279: pd.Timestamp("2013-02-05 00:00:00"),
            280: pd.Timestamp("2013-02-06 00:00:00"),
            281: pd.Timestamp("2013-02-07 00:00:00"),
            282: pd.Timestamp("2013-02-08 00:00:00"),
            283: pd.Timestamp("2013-02-09 00:00:00"),
            284: pd.Timestamp("2013-02-10 00:00:00"),
            285: pd.Timestamp("2013-02-11 00:00:00"),
            286: pd.Timestamp("2013-02-12 00:00:00"),
            287: pd.Timestamp("2013-02-13 00:00:00"),
            288: pd.Timestamp("2013-02-14 00:00:00"),
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            290: pd.Timestamp("2013-02-16 00:00:00"),
            291: pd.Timestamp("2013-02-17 00:00:00"),
            292: pd.Timestamp("2013-02-18 00:00:00"),
            293: pd.Timestamp("2013-02-19 00:00:00"),
            294: pd.Timestamp("2013-02-20 00:00:00"),
            295: pd.Timestamp("2013-02-21 00:00:00"),
            296: pd.Timestamp("2013-02-22 00:00:00"),
            297: pd.Timestamp("2013-02-23 00:00:00"),
            298: pd.Timestamp("2013-02-24 00:00:00"),
            299: pd.Timestamp("2013-02-25 00:00:00"),
            300: pd.Timestamp("2013-02-26 00:00:00"),
            301: pd.Timestamp("2013-02-27 00:00:00"),
            302: pd.Timestamp("2013-02-28 00:00:00"),
            303: pd.Timestamp("2013-03-01 00:00:00"),
            304: pd.Timestamp("2013-03-02 00:00:00"),
            305: pd.Timestamp("2013-03-03 00:00:00"),
            306: pd.Timestamp("2013-03-04 00:00:00"),
            307: pd.Timestamp("2013-03-05 00:00:00"),
            308: pd.Timestamp("2013-03-06 00:00:00"),
            309: pd.Timestamp("2013-03-07 00:00:00"),
            310: pd.Timestamp("2013-03-08 00:00:00"),
            311: pd.Timestamp("2013-03-09 00:00:00"),
            312: pd.Timestamp("2013-03-10 00:00:00"),
            313: pd.Timestamp("2013-03-11 00:00:00"),
            314: pd.Timestamp("2013-03-12 00:00:00"),
            315: pd.Timestamp("2013-03-13 00:00:00"),
            316: pd.Timestamp("2013-03-14 00:00:00"),
            317: pd.Timestamp("2013-03-15 00:00:00"),
            318: pd.Timestamp("2013-03-16 00:00:00"),
            319: pd.Timestamp("2013-03-17 00:00:00"),
            320: pd.Timestamp("2013-03-18 00:00:00"),
            321: pd.Timestamp("2013-03-19 00:00:00"),
            322: pd.Timestamp("2013-03-20 00:00:00"),
            323: pd.Timestamp("2013-03-21 00:00:00"),
            324: pd.Timestamp("2013-03-22 00:00:00"),
            325: pd.Timestamp("2013-03-23 00:00:00"),
            326: pd.Timestamp("2013-03-24 00:00:00"),
            327: pd.Timestamp("2013-03-25 00:00:00"),
            328: pd.Timestamp("2013-03-26 00:00:00"),
            329: pd.Timestamp("2013-03-27 00:00:00"),
            330: pd.Timestamp("2013-03-28 00:00:00"),
            331: pd.Timestamp("2013-03-29 00:00:00"),
            332: pd.Timestamp("2013-03-30 00:00:00"),
            333: pd.Timestamp("2013-03-31 00:00:00"),
            334: pd.Timestamp("2013-04-01 00:00:00"),
            335: pd.Timestamp("2013-04-02 00:00:00"),
            336: pd.Timestamp("2013-04-03 00:00:00"),
            337: pd.Timestamp("2013-04-04 00:00:00"),
            338: pd.Timestamp("2013-04-05 00:00:00"),
            339: pd.Timestamp("2013-04-06 00:00:00"),
            340: pd.Timestamp("2013-04-07 00:00:00"),
            341: pd.Timestamp("2013-04-08 00:00:00"),
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            350: pd.Timestamp("2013-04-17 00:00:00"),
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            377: pd.Timestamp("2013-05-14 00:00:00"),
            378: pd.Timestamp("2013-05-15 00:00:00"),
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            352: 6.894016993309594,
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            354: 7.187787676857383,
            355: 7.281651448923958,
            356: 6.968407780693915,
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            358: 6.759049271476629,
            359: 6.746533634980084,
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            361: 7.056619399387594,
            362: 7.1888396308704055,
            363: 6.873761084451768,
            364: 6.641720925457513,
            365: 6.666223534272653,
            366: 6.671861561200792,
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            375: 6.86452629031848,
            376: 7.003492305682654,
            377: 6.67653065285822,
            378: 6.435170617705394,
        },
        "fcst_upper": {
            0: 8.292007309753227,
            1: 8.368903143698532,
            2: 8.386954266821222,
            3: 8.23647291349591,
            4: 8.66376645243186,
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            334: 8.760685567997598,
            335: 8.392237649201109,
            336: 8.206304649220911,
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            338: 8.25640946409948,
            339: 8.105298750909277,
            340: 8.519866063860858,
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            342: 8.342670091276547,
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            344: 8.165501419285457,
            345: 8.148997572914627,
            346: 7.986395996304026,
            347: 8.435877600536484,
            348: 8.509211987199985,
            349: 8.216105655079616,
            350: 7.9727794342797,
            351: 8.051192042949184,
            352: 8.043434869830364,
            353: 7.844105416634229,
            354: 8.405438418000418,
            355: 8.434901218174598,
            356: 8.118705841475258,
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            358: 7.920218450798264,
            359: 7.938016873037496,
            360: 7.798140187153802,
            361: 8.262490708495418,
            362: 8.339660689166566,
            363: 8.045953862334798,
            364: 7.816543856473682,
            365: 7.800372627825619,
            366: 7.844042143291405,
            367: 7.703344139291474,
            368: 8.139957244784707,
            369: 8.277227864281219,
            370: 7.963095586509671,
            371: 7.687903761184175,
            372: 7.745763727882769,
            373: 7.809232538790762,
            374: 7.611919715499811,
            375: 8.009429203331072,
            376: 8.169063559004465,
            377: 7.819845402188427,
            378: 7.604836996007125,
        },
    }
)

AIR_FCST_15_PROPHET_LOGISTIC_CAP_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
        },
        "fcst": {
            0: 474.8363017288656,
            1: 468.99171117849556,
            2: 502.0107150977976,
            3: 501.49961364980686,
            4: 505.7999464448948,
            5: 547.8463030880213,
            6: 587.6968798959065,
            7: 588.3403482152739,
            8: 539.7409068282652,
            9: 505.70514065476294,
            10: 471.8830792741277,
            11: 501.7918634254075,
            12: 514.935717783898,
            13: 509.0208355173735,
            14: 545.3488204468623,
        },
        "fcst_lower": {
            0: 445.08242175237444,
            1: 441.72751709909295,
            2: 470.55691169739845,
            3: 474.0525321394879,
            4: 478.2321425027879,
            5: 518.8270217221966,
            6: 559.1550095543314,
            7: 559.2980056250977,
            8: 511.6934102435192,
            9: 480.81906454294034,
            10: 442.61996850681754,
            11: 473.58061234926606,
            12: 484.36591417477575,
            13: 479.8175916881218,
            14: 517.8966869136175,
        },
        "fcst_upper": {
            0: 503.8120645009746,
            1: 498.3423255535767,
            2: 530.9445170423978,
            3: 532.0118942263415,
            4: 532.5239830355447,
            5: 576.0691450083493,
            6: 617.405134654996,
            7: 617.1023541799481,
            8: 567.7339721571983,
            9: 534.7247367000513,
            10: 499.73784308244615,
            11: 529.0246435279244,
            12: 543.5705482455107,
            13: 537.592677202221,
            14: 574.6001384569872,
        },
    }
)

PEYTON_FCST_30_PROPHET_DAILY_CAP_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.309701804674974,
            1: 7.332130661074342,
            2: 7.36764685017488,
            3: 7.187538991723003,
            4: 7.6364256219589155,
            5: 7.7649637273330665,
            6: 7.462621854852071,
            7: 7.224065560790778,
            8: 7.246552162060077,
            9: 7.282126330981348,
            10: 7.10207668658457,
            11: 7.5510217643915425,
            12: 7.6796185501343315,
            13: 7.377335590095167,
            14: 7.138838439826197,
            15: 7.16138441551181,
            16: 7.197018188744356,
            17: 7.017028377823432,
            18: 7.466033517539327,
            19: 7.594690592888185,
            20: 7.292468149417277,
            21: 7.054031741940442,
            22: 7.076638685900331,
            23: 7.112333652148555,
            24: 6.932405258240528,
            25: 7.381472038219104,
            26: 7.510190976335489,
            27: 7.208030617385161,
            28: 6.969656515430458,
            29: 6.992325985161949,
        },
        "fcst_lower": {
            0: 6.664679118887306,
            1: 6.6935062966607095,
            2: 6.734868361194695,
            3: 6.586846703023675,
            4: 7.024514620228759,
            5: 7.095533934909643,
            6: 6.884381951793827,
            7: 6.614058556523093,
            8: 6.614995860763193,
            9: 6.671628963387851,
            10: 6.4987763334536055,
            11: 6.955403477927713,
            12: 7.0490801036568795,
            13: 6.738989529460044,
            14: 6.517742315745279,
            15: 6.5916072379030695,
            16: 6.622692162098093,
            17: 6.346664742045041,
            18: 6.881641988019506,
            19: 7.004916292163072,
            20: 6.6606068489546555,
            21: 6.429378890162921,
            22: 6.455593468076069,
            23: 6.478560580057457,
            24: 6.355078775015013,
            25: 6.793512368343319,
            26: 6.889681715060315,
            27: 6.536936770051854,
            28: 6.32894734006989,
            29: 6.464396545834131,
        },
        "fcst_upper": {
            0: 7.924619899634765,
            1: 7.909939503205977,
            2: 7.990955047677133,
            3: 7.841224880695751,
            4: 8.272945285721484,
            5: 8.365619528575252,
            6: 8.0661006527869,
            7: 7.832055885696028,
            8: 7.815732760844865,
            9: 7.861941621187907,
            10: 7.719764315473959,
            11: 8.12714568338086,
            12: 8.229516419349281,
            13: 7.966065783938846,
            14: 7.7507129829672765,
            15: 7.752526155549491,
            16: 7.821471477029241,
            17: 7.608593337773231,
            18: 8.074147125043737,
            19: 8.18157577742058,
            20: 7.8745727933723755,
            21: 7.622286788415174,
            22: 7.681828983075624,
            23: 7.728809396951798,
            24: 7.548825990498768,
            25: 8.005467896603225,
            26: 8.128146048791287,
            27: 7.814187288136758,
            28: 7.5655046616171955,
            29: 7.588410903530529,
        },
    }
)

AIR_FCST_30_PROPHET_CUSTOM_SEASONALITY_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 452.96601621157333,
            1: 437.98832908311806,
            2: 566.4927510503155,
            3: 533.8909937431466,
            4: 567.9188293074376,
            5: 578.0143045952303,
            6: 647.6075715186254,
            7: 616.956642348948,
            8: 542.4720010345447,
            9: 532.8286114704305,
            10: 473.2093212270391,
            11: 527.8141700989343,
            12: 510.76906094986526,
            13: 483.2323136428713,
            14: 669.501284331564,
            15: 632.0560306519425,
            16: 671.3362288462214,
            17: 677.1212663968699,
            18: 750.7019844622027,
            19: 717.0699797805638,
            20: 632.5726183830741,
            21: 632.2576551688128,
            22: 562.6499432667736,
            23: 627.7175584666347,
            24: 599.7503585672704,
            25: 557.5602199713626,
            26: 745.6397414808766,
            27: 724.8009277979628,
            28: 747.9457991040732,
            29: 770.8109509671146,
        },
        "fcst_lower": {
            0: 424.8994717774092,
            1: 412.7019590055081,
            2: 538.544143057207,
            3: 505.6848176776265,
            4: 540.9866117246912,
            5: 548.2543036094157,
            6: 621.3748034389309,
            7: 591.5108120742709,
            8: 517.8498825443983,
            9: 506.89994251860844,
            10: 445.60613912114513,
            11: 500.4500001017916,
            12: 483.92117750254266,
            13: 456.64261439964093,
            14: 641.6349008157191,
            15: 606.1229381167503,
            16: 644.0711070774304,
            17: 650.2743200642118,
            18: 723.2366681762659,
            19: 689.6957582030766,
            20: 606.866335365676,
            21: 603.5654860518345,
            22: 535.5166190823288,
            23: 599.7006883692071,
            24: 574.555533493021,
            25: 528.3118986328117,
            26: 720.9669325631302,
            27: 696.311019044141,
            28: 722.2598973215389,
            29: 744.7121674124865,
        },
        "fcst_upper": {
            0: 479.11198647133193,
            1: 463.92056712896846,
            2: 592.7841465127738,
            3: 560.4412881535918,
            4: 593.7407294863663,
            5: 605.1479186815375,
            6: 674.873476971475,
            7: 645.9211692372113,
            8: 570.0325013586959,
            9: 560.2125174120044,
            10: 498.6001137647761,
            11: 554.4479495175004,
            12: 537.6414343726829,
            13: 509.16922751615664,
            14: 695.2856840314337,
            15: 659.424463561998,
            16: 695.8442702379866,
            17: 703.9579725859626,
            18: 777.3333892567645,
            19: 745.1617634118725,
            20: 658.7770256084456,
            21: 660.1527419546438,
            22: 589.5899932592852,
            23: 654.2140626474315,
            24: 625.3790538143977,
            25: 584.0289841837368,
            26: 772.7044239821412,
            27: 750.7329886534386,
            28: 777.1858875813338,
            29: 797.4644292161097,
        },
    }
)

PEYTON_FCST_30_PROPHET_CUSTOM_SEASONALITY_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.310204404948854,
            1: 7.305480197360137,
            2: 7.30965923862747,
            3: 7.094514957955274,
            4: 7.506039017835247,
            5: 7.594800909807456,
            6: 7.250966171095805,
            7: 6.965193791651218,
            8: 6.946210113397585,
            9: 6.941352852627586,
            10: 6.722546864139355,
            11: 7.135794857559768,
            12: 7.231537700553363,
            13: 6.899677113462629,
            14: 6.6304811880089405,
            15: 6.632169794832014,
            16: 6.65147318062203,
            17: 6.459624959467439,
            18: 6.901872222951885,
            19: 7.027856859645095,
            20: 6.726660301756082,
            21: 6.487731518608982,
            22: 6.518494699876626,
            23: 6.564929316021729,
            24: 6.397583172958472,
            25: 6.86110063995964,
            26: 7.004619653126363,
            27: 6.71682929447113,
            28: 6.48690743062501,
            29: 6.52213460943214,
        },
        "fcst_lower": {
            0: 6.726788786466419,
            1: 6.727851787147943,
            2: 6.737279515903056,
            3: 6.550498073046225,
            4: 6.952572537668496,
            5: 6.989309343413395,
            6: 6.728383343554542,
            7: 6.410635004556951,
            8: 6.379215999991831,
            9: 6.393439501498509,
            10: 6.176868616834364,
            11: 6.597108447865797,
            12: 6.660090046305426,
            13: 6.322017466338105,
            14: 6.066743091435035,
            15: 6.115686889613311,
            16: 6.136187500488641,
            17: 5.85146603255074,
            18: 6.375089849528449,
            19: 6.4910400994583,
            20: 6.153054105616886,
            21: 5.92286419233778,
            22: 5.954450199189329,
            23: 5.9868526231575,
            24: 5.867070586271821,
            25: 6.330352633577601,
            26: 6.440787940475338,
            27: 6.104088447438135,
            28: 5.89604671300344,
            29: 6.042822632291787,
        },
        "fcst_upper": {
            0: 7.866390766439251,
            1: 7.828101665448806,
            2: 7.873434351601799,
            3: 7.685766344319408,
            4: 8.081832080581423,
            5: 8.138087190271907,
            6: 7.798470261770245,
            7: 7.515399332903971,
            8: 7.461993576122207,
            9: 7.4691123124526095,
            10: 7.279958001655828,
            11: 7.660532609846931,
            12: 7.729517766876632,
            13: 7.432711662424305,
            14: 7.1861107442967205,
            15: 7.162436387583534,
            16: 7.216365260664922,
            17: 6.991879095326979,
            18: 7.44636612975707,
            19: 7.562234428757602,
            20: 7.253525743078088,
            21: 7.003887250880256,
            22: 7.063487442697169,
            23: 7.119021167343828,
            24: 6.955999042940917,
            25: 7.4251575313642775,
            26: 7.565136033469522,
            27: 7.270713445151623,
            28: 7.029987420401476,
            29: 7.062272300962427,
        },
    }
)

NONSEASONAL_FCST_15_PROPHET_ARG_FUTURE_SM_11 = pd.DataFrame(
    {
        "ds": {
            0: pd.Timestamp("1963-01-31 00:00:00"),
            1: pd.Timestamp("1963-02-28 00:00:00"),
            2: pd.Timestamp("1963-03-31 00:00:00"),
            3: pd.Timestamp("1963-04-30 00:00:00"),
            4: pd.Timestamp("1963-05-31 00:00:00"),
            5: pd.Timestamp("1963-06-30 00:00:00"),
            6: pd.Timestamp("1963-07-31 00:00:00"),
            7: pd.Timestamp("1963-08-31 00:00:00"),
            8: pd.Timestamp("1963-09-30 00:00:00"),
            9: pd.Timestamp("1963-10-31 00:00:00"),
            10: pd.Timestamp("1963-11-30 00:00:00"),
            11: pd.Timestamp("1963-12-31 00:00:00"),
        },
        "trend": {
            0: 1.4863506035356537,
            1: 1.5153722851288405,
            2: 1.5475034326070118,
            3: 1.5785980914568547,
            4: 1.610729238935026,
            5: 1.6418238977848691,
            6: 1.6739550452630403,
            7: 1.7060861927412116,
            8: 1.7371808515910547,
            9: 1.7693119990692259,
            10: 1.8004066579190692,
            11: 1.83253780539724,
        },
        "yhat_lower": {
            0: 1.1594374527914133,
            1: -2.248335089417451,
            2: 2.0604229200350743,
            3: 2.282279023693326,
            4: 1.2809544896936165,
            5: -0.3992332220272177,
            6: -3.552547372714546,
            7: 2.429955426700173,
            8: 2.9000236398478862,
            9: 0.6974054961124588,
            10: 0.9017089206384991,
            11: 0.9537477781206941,
        },
        "yhat_upper": {
            0: 1.6201165163554494,
            1: -1.7455747337323577,
            2: 2.531809100253131,
            3: 2.777959505427363,
            4: 1.7376132983833892,
            5: 0.07946819642707456,
            6: -3.0853192468466877,
            7: 2.8960848991428243,
            8: 3.3712332482620138,
            9: 1.1821260937994622,
            10: 1.3997416814227919,
            11: 1.4474825924984773,
        },
        "trend_lower": {
            0: 1.486350550058763,
            1: 1.5153720936439148,
            2: 1.5475030409711172,
            3: 1.5785974767875617,
            4: 1.6107283337535407,
            5: 1.6418227060078137,
            6: 1.6739535322032517,
            7: 1.706084373856565,
            8: 1.7371786410770564,
            9: 1.769309338677681,
            10: 1.8004035415083601,
            11: 1.832534164520744,
        },
        "trend_upper": {
            0: 1.486350659928231,
            1: 1.515372451724875,
            2: 1.5475037804692382,
            3: 1.5785986386258855,
            4: 1.6107300179980912,
            5: 1.6418249316742897,
            6: 1.6739563806098692,
            7: 1.7060878674683846,
            8: 1.737182890758364,
            9: 1.7693143896963959,
            10: 1.800409447090903,
            11: 1.8325410022110504,
        },
        "additive_terms": {
            0: -0.10603153621913787,
            1: -3.5100601864408625,
            2: 0.7525595383683356,
            3: 0.9495977533373023,
            4: -0.08872196007671039,
            5: -1.7897575752775112,
            6: -4.992084112210778,
            7: 0.9439920445988806,
            8: 1.410764220309051,
            9: -0.8269897561991282,
            10: -0.6332794813823102,
            11: -0.627787096526166,
        },
        "additive_terms_lower": {
            0: -0.10603153621913787,
            1: -3.5100601864408625,
            2: 0.7525595383683356,
            3: 0.9495977533373023,
            4: -0.08872196007671039,
            5: -1.7897575752775112,
            6: -4.992084112210778,
            7: 0.9439920445988806,
            8: 1.410764220309051,
            9: -0.8269897561991282,
            10: -0.6332794813823102,
            11: -0.627787096526166,
        },
        "additive_terms_upper": {
            0: -0.10603153621913787,
            1: -3.5100601864408625,
            2: 0.7525595383683356,
            3: 0.9495977533373023,
            4: -0.08872196007671039,
            5: -1.7897575752775112,
            6: -4.992084112210778,
            7: 0.9439920445988806,
            8: 1.410764220309051,
            9: -0.8269897561991282,
            10: -0.6332794813823102,
            11: -0.627787096526166,
        },
        "yearly": {
            0: -0.10603153621913787,
            1: -3.5100601864408625,
            2: 0.7525595383683356,
            3: 0.9495977533373023,
            4: -0.08872196007671039,
            5: -1.7897575752775112,
            6: -4.992084112210778,
            7: 0.9439920445988806,
            8: 1.410764220309051,
            9: -0.8269897561991282,
            10: -0.6332794813823102,
            11: -0.627787096526166,
        },
        "yearly_lower": {
            0: -0.10603153621913787,
            1: -3.5100601864408625,
            2: 0.7525595383683356,
            3: 0.9495977533373023,
            4: -0.08872196007671039,
            5: -1.7897575752775112,
            6: -4.992084112210778,
            7: 0.9439920445988806,
            8: 1.410764220309051,
            9: -0.8269897561991282,
            10: -0.6332794813823102,
            11: -0.627787096526166,
        },
        "yearly_upper": {
            0: -0.10603153621913787,
            1: -3.5100601864408625,
            2: 0.7525595383683356,
            3: 0.9495977533373023,
            4: -0.08872196007671039,
            5: -1.7897575752775112,
            6: -4.992084112210778,
            7: 0.9439920445988806,
            8: 1.410764220309051,
            9: -0.8269897561991282,
            10: -0.6332794813823102,
            11: -0.627787096526166,
        },
        "multiplicative_terms": {
            0: 0.0,
            1: 0.0,
            2: 0.0,
            3: 0.0,
            4: 0.0,
            5: 0.0,
            6: 0.0,
            7: 0.0,
            8: 0.0,
            9: 0.0,
            10: 0.0,
            11: 0.0,
        },
        "multiplicative_terms_lower": {
            0: 0.0,
            1: 0.0,
            2: 0.0,
            3: 0.0,
            4: 0.0,
            5: 0.0,
            6: 0.0,
            7: 0.0,
            8: 0.0,
            9: 0.0,
            10: 0.0,
            11: 0.0,
        },
        "multiplicative_terms_upper": {
            0: 0.0,
            1: 0.0,
            2: 0.0,
            3: 0.0,
            4: 0.0,
            5: 0.0,
            6: 0.0,
            7: 0.0,
            8: 0.0,
            9: 0.0,
            10: 0.0,
            11: 0.0,
        },
        "yhat": {
            0: 1.3803190673165158,
            1: -1.994687901312022,
            2: 2.3000629709753473,
            3: 2.528195844794157,
            4: 1.5220072788583157,
            5: -0.14793367749264208,
            6: -3.3181290669477375,
            7: 2.650078237340092,
            8: 3.1479450719001054,
            9: 0.9423222428700977,
            10: 1.167127176536759,
            11: 1.204750708871074,
        },
    }
)

AIR_FCST_30_PROPHET_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 466.54949529306384,
            1: 461.0149722219397,
            2: 493.42051383362895,
            3: 492.09716300129605,
            4: 496.43069102104533,
            5: 537.5706104154885,
            6: 577.1448049907949,
            7: 577.5779251624626,
            8: 529.0042295821432,
            9: 493.8507852015329,
            10: 459.98717217484693,
            11: 489.352454886114,
            12: 502.3800115074687,
            13: 496.27360400418974,
            14: 531.9184998721412,
            15: 528.0143396522675,
            16: 534.1153773178809,
            17: 573.5694202090814,
            18: 614.189938977541,
            19: 614.1506680384373,
            20: 566.2293188085794,
            21: 530.5315659720388,
            22: 497.68269580029374,
            23: 527.2074641183364,
            24: 538.1711703676725,
            25: 531.5641426276097,
            26: 570.4184320916362,
            27: 563.9262064953872,
            28: 571.8037322668091,
            29: 609.5581957151934,
        },
        "fcst_lower": {
            0: 436.66543323521887,
            1: 434.08218961824196,
            2: 463.69220344096215,
            3: 462.0751659406037,
            4: 467.76598827119955,
            5: 505.8520356783214,
            6: 549.3141576606173,
            7: 550.3899546190513,
            8: 502.81872758510326,
            9: 466.24020432761296,
            10: 430.4665703998807,
            11: 460.16737670536145,
            12: 473.8596106354481,
            13: 468.17109439503025,
            14: 502.9592032377311,
            15: 500.95868572567764,
            16: 505.11654752091164,
            17: 545.4451699085795,
            18: 584.9424565920709,
            19: 585.0593457336159,
            20: 538.8098189878858,
            21: 499.74638371229423,
            22: 468.8232517708133,
            23: 497.2896705579375,
            24: 511.45671004444404,
            25: 500.3644211985417,
            26: 544.0015236741305,
            27: 534.1427559429333,
            28: 544.0144882894072,
            29: 581.7969591787996,
        },
        "fcst_upper": {
            0: 494.38861170577394,
            1: 488.5518870859255,
            2: 521.3122112469476,
            3: 520.4399102319655,
            4: 523.931005158026,
            5: 566.178119883852,
            6: 606.1669026098009,
            7: 608.3477325943362,
            8: 558.2332904556636,
            9: 522.882506512789,
            10: 487.2921618559126,
            11: 517.7368606824481,
            12: 530.8435907663193,
            13: 524.1896920778432,
            14: 559.6675371699441,
            15: 557.474799838249,
            16: 560.5897837084689,
            17: 602.3868489857199,
            18: 642.2968195734655,
            19: 644.030142013098,
            20: 594.2218553261007,
            21: 560.2950574381408,
            22: 526.9077147025658,
            23: 555.4253978133032,
            24: 565.2390807788803,
            25: 560.0229214856699,
            26: 599.8227436725223,
            27: 592.1244528776316,
            28: 603.0971896013723,
            29: 638.7723104070045,
        },
    }
)

AIR_FCST_30_PROPHET_CAP_AND_FLOOR_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 475.1532782046899,
            1: 469.19332724731584,
            2: 503.09501509424797,
            3: 502.3043546207114,
            4: 506.8276560032621,
            5: 548.6664781115998,
            6: 588.6792337637517,
            7: 589.3572357297155,
            8: 541.0308636258886,
            9: 506.9540499402625,
            10: 473.20176525118853,
            11: 503.08513064927183,
            12: 516.0402922815176,
            13: 510.17911104897803,
            14: 546.4591808241485,
            15: 542.9239266057684,
            16: 549.7698203847091,
            17: 589.4202040295114,
            18: 630.4119972016963,
            19: 630.9237637324917,
            20: 583.8723702813984,
            21: 547.8718370084842,
            22: 515.6608558103559,
            23: 545.1869851982699,
            24: 556.4160884170091,
            25: 550.6603609242528,
            26: 589.2299878549468,
            27: 582.9070929557129,
            28: 592.0357124889904,
            29: 629.4305563722146,
        },
        "fcst_lower": {
            0: 445.3395443711149,
            1: 442.33283828047774,
            2: 473.4065517864172,
            3: 472.3422733141102,
            4: 478.2188373929017,
            5: 517.0541830910526,
            6: 560.8132337095608,
            7: 562.3266592520714,
            8: 514.8766805436387,
            9: 479.41117903767577,
            10: 443.8803251671445,
            11: 474.0175314026396,
            12: 487.5212766527527,
            13: 481.9349719051492,
            14: 516.8614551262343,
            15: 515.3770745638952,
            16: 520.8065583984169,
            17: 560.9024345724439,
            18: 601.2367939715982,
            19: 601.844916480008,
            20: 556.5629188883239,
            21: 517.3902520523155,
            22: 486.83777806666336,
            23: 515.4270272174365,
            24: 529.6516110502463,
            25: 519.5898993143903,
            26: 563.0260162280957,
            27: 552.6444610036451,
            28: 564.7562214012846,
            29: 601.7093827934974,
        },
        "fcst_upper": {
            0: 502.9268789047515,
            1: 496.74000386332597,
            2: 531.0230939594485,
            3: 530.50745194603,
            4: 534.257007624121,
            5: 577.4892366293137,
            6: 617.6425436630758,
            7: 620.125016753491,
            8: 570.3073098748073,
            9: 536.042712577469,
            10: 500.17320072264005,
            11: 531.3768629902295,
            12: 544.5891685624433,
            13: 537.7300273966486,
            14: 573.8500080233164,
            15: 571.99667589684,
            16: 575.8038948645637,
            17: 617.9323140966267,
            18: 658.7012733979813,
            19: 660.7630127843707,
            20: 611.7124422004916,
            21: 577.5027972451942,
            22: 544.2847324665361,
            23: 573.3319938049964,
            24: 583.644640726565,
            25: 578.7784734053895,
            26: 617.9831777957173,
            27: 610.4530629518694,
            28: 623.0946907933026,
            29: 657.7480443198558,
        },
    }
)

PEYTON_FCST_30_PROPHET_CAP_AND_FLOOR_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 9.86101525225098,
            1: 9.89003240995565,
            2: 9.930278231857494,
            3: 9.784139044260016,
            4: 10.181742594102822,
            5: 10.302363217267562,
            6: 10.050429250306896,
            7: 9.861015252250708,
            8: 9.890032409955243,
            9: 9.930278231857312,
            10: 9.784139044259918,
            11: 10.18174259410229,
            12: 10.30236321726758,
            13: 10.050429250306546,
            14: 9.861015252250434,
            15: 9.890032409955339,
            16: 9.930278231857514,
            17: 9.78413904425997,
            18: 10.181742594102731,
            19: 10.302363217267596,
            20: 10.05042925030657,
            21: 9.861015252250867,
            22: 9.890032409955339,
            23: 9.930278231857436,
            24: 9.784139044260082,
            25: 10.181742594101985,
            26: 10.302363217267446,
            27: 10.050429250306577,
            28: 9.861015252250596,
            29: 9.890032409955337,
        },
        "fcst_lower": {
            0: 7.523186110258983,
            1: 7.575393432430172,
            2: 7.635561729245729,
            3: 7.625208439274192,
            4: 7.963923897690048,
            5: 7.876072620474026,
            6: 7.953465125739613,
            7: 7.652371536131636,
            8: 7.619598524007825,
            9: 7.727352391847119,
            10: 7.5905279492768365,
            11: 8.023666366421168,
            12: 8.020968139682537,
            13: 7.747017836622224,
            14: 7.629569241306196,
            15: 7.8357356664953866,
            16: 7.870761785018109,
            17: 7.392173418510056,
            18: 8.072712387199624,
            19: 8.164211694017458,
            20: 7.753943791765615,
            21: 7.625176023048917,
            22: 7.6396263868463645,
            23: 7.630128907307829,
            24: 7.64113995264005,
            25: 8.028837974013635,
            26: 8.01673543963455,
            27: 7.683822540109567,
            28: 7.561195117626147,
            29: 7.995948172252461,
        },
        "fcst_upper": {
            0: 12.08973289894849,
            1: 11.98425076754169,
            2: 12.190423304929823,
            3: 12.153367219674141,
            4: 12.48755294099703,
            5: 12.479388409390472,
            6: 12.237686150680116,
            7: 12.06206958990392,
            8: 11.960677102502245,
            9: 12.055337762535864,
            10: 12.037647959991915,
            11: 12.28240533219545,
            12: 12.29542065865066,
            13: 12.20603517755278,
            14: 12.103815485868521,
            15: 12.035622752377364,
            16: 12.198735176204378,
            17: 11.922095490841548,
            18: 12.38307243375836,
            19: 12.435352493663697,
            20: 12.147049326147211,
            21: 11.962957077824154,
            22: 12.081392902111746,
            23: 12.175395109838217,
            24: 11.998325144545058,
            25: 12.441980194908517,
            26: 12.502881352045287,
            27: 12.281079278334424,
            28: 12.016659812808099,
            29: 12.02169036520016,
        },
    }
)

AIR_FCST_30_PROPHET_INCL_HIST_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1949-01-01 00:00:00"),
            1: pd.Timestamp("1949-02-01 00:00:00"),
            2: pd.Timestamp("1949-03-01 00:00:00"),
            3: pd.Timestamp("1949-04-01 00:00:00"),
            4: pd.Timestamp("1949-05-01 00:00:00"),
            5: pd.Timestamp("1949-06-01 00:00:00"),
            6: pd.Timestamp("1949-07-01 00:00:00"),
            7: pd.Timestamp("1949-08-01 00:00:00"),
            8: pd.Timestamp("1949-09-01 00:00:00"),
            9: pd.Timestamp("1949-10-01 00:00:00"),
            10: pd.Timestamp("1949-11-01 00:00:00"),
            11: pd.Timestamp("1949-12-01 00:00:00"),
            12: pd.Timestamp("1950-01-01 00:00:00"),
            13: pd.Timestamp("1950-02-01 00:00:00"),
            14: pd.Timestamp("1950-03-01 00:00:00"),
            15: pd.Timestamp("1950-04-01 00:00:00"),
            16: pd.Timestamp("1950-05-01 00:00:00"),
            17: pd.Timestamp("1950-06-01 00:00:00"),
            18: pd.Timestamp("1950-07-01 00:00:00"),
            19: pd.Timestamp("1950-08-01 00:00:00"),
            20: pd.Timestamp("1950-09-01 00:00:00"),
            21: pd.Timestamp("1950-10-01 00:00:00"),
            22: pd.Timestamp("1950-11-01 00:00:00"),
            23: pd.Timestamp("1950-12-01 00:00:00"),
            24: pd.Timestamp("1951-01-01 00:00:00"),
            25: pd.Timestamp("1951-02-01 00:00:00"),
            26: pd.Timestamp("1951-03-01 00:00:00"),
            27: pd.Timestamp("1951-04-01 00:00:00"),
            28: pd.Timestamp("1951-05-01 00:00:00"),
            29: pd.Timestamp("1951-06-01 00:00:00"),
            30: pd.Timestamp("1951-07-01 00:00:00"),
            31: pd.Timestamp("1951-08-01 00:00:00"),
            32: pd.Timestamp("1951-09-01 00:00:00"),
            33: pd.Timestamp("1951-10-01 00:00:00"),
            34: pd.Timestamp("1951-11-01 00:00:00"),
            35: pd.Timestamp("1951-12-01 00:00:00"),
            36: pd.Timestamp("1952-01-01 00:00:00"),
            37: pd.Timestamp("1952-02-01 00:00:00"),
            38: pd.Timestamp("1952-03-01 00:00:00"),
            39: pd.Timestamp("1952-04-01 00:00:00"),
            40: pd.Timestamp("1952-05-01 00:00:00"),
            41: pd.Timestamp("1952-06-01 00:00:00"),
            42: pd.Timestamp("1952-07-01 00:00:00"),
            43: pd.Timestamp("1952-08-01 00:00:00"),
            44: pd.Timestamp("1952-09-01 00:00:00"),
            45: pd.Timestamp("1952-10-01 00:00:00"),
            46: pd.Timestamp("1952-11-01 00:00:00"),
            47: pd.Timestamp("1952-12-01 00:00:00"),
            48: pd.Timestamp("1953-01-01 00:00:00"),
            49: pd.Timestamp("1953-02-01 00:00:00"),
            50: pd.Timestamp("1953-03-01 00:00:00"),
            51: pd.Timestamp("1953-04-01 00:00:00"),
            52: pd.Timestamp("1953-05-01 00:00:00"),
            53: pd.Timestamp("1953-06-01 00:00:00"),
            54: pd.Timestamp("1953-07-01 00:00:00"),
            55: pd.Timestamp("1953-08-01 00:00:00"),
            56: pd.Timestamp("1953-09-01 00:00:00"),
            57: pd.Timestamp("1953-10-01 00:00:00"),
            58: pd.Timestamp("1953-11-01 00:00:00"),
            59: pd.Timestamp("1953-12-01 00:00:00"),
            60: pd.Timestamp("1954-01-01 00:00:00"),
            61: pd.Timestamp("1954-02-01 00:00:00"),
            62: pd.Timestamp("1954-03-01 00:00:00"),
            63: pd.Timestamp("1954-04-01 00:00:00"),
            64: pd.Timestamp("1954-05-01 00:00:00"),
            65: pd.Timestamp("1954-06-01 00:00:00"),
            66: pd.Timestamp("1954-07-01 00:00:00"),
            67: pd.Timestamp("1954-08-01 00:00:00"),
            68: pd.Timestamp("1954-09-01 00:00:00"),
            69: pd.Timestamp("1954-10-01 00:00:00"),
            70: pd.Timestamp("1954-11-01 00:00:00"),
            71: pd.Timestamp("1954-12-01 00:00:00"),
            72: pd.Timestamp("1955-01-01 00:00:00"),
            73: pd.Timestamp("1955-02-01 00:00:00"),
            74: pd.Timestamp("1955-03-01 00:00:00"),
            75: pd.Timestamp("1955-04-01 00:00:00"),
            76: pd.Timestamp("1955-05-01 00:00:00"),
            77: pd.Timestamp("1955-06-01 00:00:00"),
            78: pd.Timestamp("1955-07-01 00:00:00"),
            79: pd.Timestamp("1955-08-01 00:00:00"),
            80: pd.Timestamp("1955-09-01 00:00:00"),
            81: pd.Timestamp("1955-10-01 00:00:00"),
            82: pd.Timestamp("1955-11-01 00:00:00"),
            83: pd.Timestamp("1955-12-01 00:00:00"),
            84: pd.Timestamp("1956-01-01 00:00:00"),
            85: pd.Timestamp("1956-02-01 00:00:00"),
            86: pd.Timestamp("1956-03-01 00:00:00"),
            87: pd.Timestamp("1956-04-01 00:00:00"),
            88: pd.Timestamp("1956-05-01 00:00:00"),
            89: pd.Timestamp("1956-06-01 00:00:00"),
            90: pd.Timestamp("1956-07-01 00:00:00"),
            91: pd.Timestamp("1956-08-01 00:00:00"),
            92: pd.Timestamp("1956-09-01 00:00:00"),
            93: pd.Timestamp("1956-10-01 00:00:00"),
            94: pd.Timestamp("1956-11-01 00:00:00"),
            95: pd.Timestamp("1956-12-01 00:00:00"),
            96: pd.Timestamp("1957-01-01 00:00:00"),
            97: pd.Timestamp("1957-02-01 00:00:00"),
            98: pd.Timestamp("1957-03-01 00:00:00"),
            99: pd.Timestamp("1957-04-01 00:00:00"),
            100: pd.Timestamp("1957-05-01 00:00:00"),
            101: pd.Timestamp("1957-06-01 00:00:00"),
            102: pd.Timestamp("1957-07-01 00:00:00"),
            103: pd.Timestamp("1957-08-01 00:00:00"),
            104: pd.Timestamp("1957-09-01 00:00:00"),
            105: pd.Timestamp("1957-10-01 00:00:00"),
            106: pd.Timestamp("1957-11-01 00:00:00"),
            107: pd.Timestamp("1957-12-01 00:00:00"),
            108: pd.Timestamp("1958-01-01 00:00:00"),
            109: pd.Timestamp("1958-02-01 00:00:00"),
            110: pd.Timestamp("1958-03-01 00:00:00"),
            111: pd.Timestamp("1958-04-01 00:00:00"),
            112: pd.Timestamp("1958-05-01 00:00:00"),
            113: pd.Timestamp("1958-06-01 00:00:00"),
            114: pd.Timestamp("1958-07-01 00:00:00"),
            115: pd.Timestamp("1958-08-01 00:00:00"),
            116: pd.Timestamp("1958-09-01 00:00:00"),
            117: pd.Timestamp("1958-10-01 00:00:00"),
            118: pd.Timestamp("1958-11-01 00:00:00"),
            119: pd.Timestamp("1958-12-01 00:00:00"),
            120: pd.Timestamp("1959-01-01 00:00:00"),
            121: pd.Timestamp("1959-02-01 00:00:00"),
            122: pd.Timestamp("1959-03-01 00:00:00"),
            123: pd.Timestamp("1959-04-01 00:00:00"),
            124: pd.Timestamp("1959-05-01 00:00:00"),
            125: pd.Timestamp("1959-06-01 00:00:00"),
            126: pd.Timestamp("1959-07-01 00:00:00"),
            127: pd.Timestamp("1959-08-01 00:00:00"),
            128: pd.Timestamp("1959-09-01 00:00:00"),
            129: pd.Timestamp("1959-10-01 00:00:00"),
            130: pd.Timestamp("1959-11-01 00:00:00"),
            131: pd.Timestamp("1959-12-01 00:00:00"),
            132: pd.Timestamp("1960-01-01 00:00:00"),
            133: pd.Timestamp("1960-02-01 00:00:00"),
            134: pd.Timestamp("1960-03-01 00:00:00"),
            135: pd.Timestamp("1960-04-01 00:00:00"),
            136: pd.Timestamp("1960-05-01 00:00:00"),
            137: pd.Timestamp("1960-06-01 00:00:00"),
            138: pd.Timestamp("1960-07-01 00:00:00"),
            139: pd.Timestamp("1960-08-01 00:00:00"),
            140: pd.Timestamp("1960-09-01 00:00:00"),
            141: pd.Timestamp("1960-10-01 00:00:00"),
            142: pd.Timestamp("1960-11-01 00:00:00"),
            143: pd.Timestamp("1960-12-01 00:00:00"),
            144: pd.Timestamp("1961-01-01 00:00:00"),
            145: pd.Timestamp("1961-02-01 00:00:00"),
            146: pd.Timestamp("1961-03-01 00:00:00"),
            147: pd.Timestamp("1961-04-01 00:00:00"),
            148: pd.Timestamp("1961-05-01 00:00:00"),
            149: pd.Timestamp("1961-06-01 00:00:00"),
            150: pd.Timestamp("1961-07-01 00:00:00"),
            151: pd.Timestamp("1961-08-01 00:00:00"),
            152: pd.Timestamp("1961-09-01 00:00:00"),
            153: pd.Timestamp("1961-10-01 00:00:00"),
            154: pd.Timestamp("1961-11-01 00:00:00"),
            155: pd.Timestamp("1961-12-01 00:00:00"),
            156: pd.Timestamp("1962-01-01 00:00:00"),
            157: pd.Timestamp("1962-02-01 00:00:00"),
            158: pd.Timestamp("1962-03-01 00:00:00"),
            159: pd.Timestamp("1962-04-01 00:00:00"),
            160: pd.Timestamp("1962-05-01 00:00:00"),
            161: pd.Timestamp("1962-06-01 00:00:00"),
            162: pd.Timestamp("1962-07-01 00:00:00"),
            163: pd.Timestamp("1962-08-01 00:00:00"),
            164: pd.Timestamp("1962-09-01 00:00:00"),
            165: pd.Timestamp("1962-10-01 00:00:00"),
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            168: pd.Timestamp("1963-01-01 00:00:00"),
            169: pd.Timestamp("1963-02-01 00:00:00"),
            170: pd.Timestamp("1963-03-01 00:00:00"),
            171: pd.Timestamp("1963-04-01 00:00:00"),
            172: pd.Timestamp("1963-05-01 00:00:00"),
            173: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
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        "fcst_lower": {
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        "fcst_upper": {
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        },
    }
)

PEYTON_FCST_15_PROPHET_INCL_HIST_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2012-05-02 00:00:00"),
            1: pd.Timestamp("2012-05-03 00:00:00"),
            2: pd.Timestamp("2012-05-04 00:00:00"),
            3: pd.Timestamp("2012-05-05 00:00:00"),
            4: pd.Timestamp("2012-05-06 00:00:00"),
            5: pd.Timestamp("2012-05-07 00:00:00"),
            6: pd.Timestamp("2012-05-08 00:00:00"),
            7: pd.Timestamp("2012-05-09 00:00:00"),
            8: pd.Timestamp("2012-05-10 00:00:00"),
            9: pd.Timestamp("2012-05-11 00:00:00"),
            10: pd.Timestamp("2012-05-12 00:00:00"),
            11: pd.Timestamp("2012-05-13 00:00:00"),
            12: pd.Timestamp("2012-05-14 00:00:00"),
            13: pd.Timestamp("2012-05-15 00:00:00"),
            14: pd.Timestamp("2012-05-16 00:00:00"),
            15: pd.Timestamp("2012-05-17 00:00:00"),
            16: pd.Timestamp("2012-05-18 00:00:00"),
            17: pd.Timestamp("2012-05-19 00:00:00"),
            18: pd.Timestamp("2012-05-20 00:00:00"),
            19: pd.Timestamp("2012-05-21 00:00:00"),
            20: pd.Timestamp("2012-05-22 00:00:00"),
            21: pd.Timestamp("2012-05-23 00:00:00"),
            22: pd.Timestamp("2012-05-24 00:00:00"),
            23: pd.Timestamp("2012-05-25 00:00:00"),
            24: pd.Timestamp("2012-05-26 00:00:00"),
            25: pd.Timestamp("2012-05-27 00:00:00"),
            26: pd.Timestamp("2012-05-28 00:00:00"),
            27: pd.Timestamp("2012-05-29 00:00:00"),
            28: pd.Timestamp("2012-05-30 00:00:00"),
            29: pd.Timestamp("2012-05-31 00:00:00"),
            30: pd.Timestamp("2012-06-01 00:00:00"),
            31: pd.Timestamp("2012-06-02 00:00:00"),
            32: pd.Timestamp("2012-06-03 00:00:00"),
            33: pd.Timestamp("2012-06-04 00:00:00"),
            34: pd.Timestamp("2012-06-05 00:00:00"),
            35: pd.Timestamp("2012-06-06 00:00:00"),
            36: pd.Timestamp("2012-06-07 00:00:00"),
            37: pd.Timestamp("2012-06-08 00:00:00"),
            38: pd.Timestamp("2012-06-09 00:00:00"),
            39: pd.Timestamp("2012-06-10 00:00:00"),
            40: pd.Timestamp("2012-06-11 00:00:00"),
            41: pd.Timestamp("2012-06-12 00:00:00"),
            42: pd.Timestamp("2012-06-13 00:00:00"),
            43: pd.Timestamp("2012-06-14 00:00:00"),
            44: pd.Timestamp("2012-06-15 00:00:00"),
            45: pd.Timestamp("2012-06-16 00:00:00"),
            46: pd.Timestamp("2012-06-17 00:00:00"),
            47: pd.Timestamp("2012-06-18 00:00:00"),
            48: pd.Timestamp("2012-06-19 00:00:00"),
            49: pd.Timestamp("2012-06-20 00:00:00"),
            50: pd.Timestamp("2012-06-21 00:00:00"),
            51: pd.Timestamp("2012-06-22 00:00:00"),
            52: pd.Timestamp("2012-06-23 00:00:00"),
            53: pd.Timestamp("2012-06-24 00:00:00"),
            54: pd.Timestamp("2012-06-25 00:00:00"),
            55: pd.Timestamp("2012-06-26 00:00:00"),
            56: pd.Timestamp("2012-06-27 00:00:00"),
            57: pd.Timestamp("2012-06-28 00:00:00"),
            58: pd.Timestamp("2012-06-29 00:00:00"),
            59: pd.Timestamp("2012-06-30 00:00:00"),
            60: pd.Timestamp("2012-07-01 00:00:00"),
            61: pd.Timestamp("2012-07-02 00:00:00"),
            62: pd.Timestamp("2012-07-03 00:00:00"),
            63: pd.Timestamp("2012-07-04 00:00:00"),
            64: pd.Timestamp("2012-07-05 00:00:00"),
            65: pd.Timestamp("2012-07-06 00:00:00"),
            66: pd.Timestamp("2012-07-07 00:00:00"),
            67: pd.Timestamp("2012-07-08 00:00:00"),
            68: pd.Timestamp("2012-07-09 00:00:00"),
            69: pd.Timestamp("2012-07-10 00:00:00"),
            70: pd.Timestamp("2012-07-11 00:00:00"),
            71: pd.Timestamp("2012-07-12 00:00:00"),
            72: pd.Timestamp("2012-07-13 00:00:00"),
            73: pd.Timestamp("2012-07-14 00:00:00"),
            74: pd.Timestamp("2012-07-15 00:00:00"),
            75: pd.Timestamp("2012-07-16 00:00:00"),
            76: pd.Timestamp("2012-07-17 00:00:00"),
            77: pd.Timestamp("2012-07-18 00:00:00"),
            78: pd.Timestamp("2012-07-19 00:00:00"),
            79: pd.Timestamp("2012-07-20 00:00:00"),
            80: pd.Timestamp("2012-07-21 00:00:00"),
            81: pd.Timestamp("2012-07-22 00:00:00"),
            82: pd.Timestamp("2012-07-23 00:00:00"),
            83: pd.Timestamp("2012-07-24 00:00:00"),
            84: pd.Timestamp("2012-07-25 00:00:00"),
            85: pd.Timestamp("2012-07-26 00:00:00"),
            86: pd.Timestamp("2012-07-27 00:00:00"),
            87: pd.Timestamp("2012-07-28 00:00:00"),
            88: pd.Timestamp("2012-07-29 00:00:00"),
            89: pd.Timestamp("2012-07-30 00:00:00"),
            90: pd.Timestamp("2012-07-31 00:00:00"),
            91: pd.Timestamp("2012-08-01 00:00:00"),
            92: pd.Timestamp("2012-08-02 00:00:00"),
            93: pd.Timestamp("2012-08-03 00:00:00"),
            94: pd.Timestamp("2012-08-04 00:00:00"),
            95: pd.Timestamp("2012-08-05 00:00:00"),
            96: pd.Timestamp("2012-08-06 00:00:00"),
            97: pd.Timestamp("2012-08-07 00:00:00"),
            98: pd.Timestamp("2012-08-08 00:00:00"),
            99: pd.Timestamp("2012-08-09 00:00:00"),
            100: pd.Timestamp("2012-08-10 00:00:00"),
            101: pd.Timestamp("2012-08-11 00:00:00"),
            102: pd.Timestamp("2012-08-12 00:00:00"),
            103: pd.Timestamp("2012-08-13 00:00:00"),
            104: pd.Timestamp("2012-08-14 00:00:00"),
            105: pd.Timestamp("2012-08-15 00:00:00"),
            106: pd.Timestamp("2012-08-16 00:00:00"),
            107: pd.Timestamp("2012-08-17 00:00:00"),
            108: pd.Timestamp("2012-08-18 00:00:00"),
            109: pd.Timestamp("2012-08-19 00:00:00"),
            110: pd.Timestamp("2012-08-20 00:00:00"),
            111: pd.Timestamp("2012-08-21 00:00:00"),
            112: pd.Timestamp("2012-08-22 00:00:00"),
            113: pd.Timestamp("2012-08-23 00:00:00"),
            114: pd.Timestamp("2012-08-24 00:00:00"),
            115: pd.Timestamp("2012-08-25 00:00:00"),
            116: pd.Timestamp("2012-08-26 00:00:00"),
            117: pd.Timestamp("2012-08-27 00:00:00"),
            118: pd.Timestamp("2012-08-28 00:00:00"),
            119: pd.Timestamp("2012-08-29 00:00:00"),
            120: pd.Timestamp("2012-08-30 00:00:00"),
            121: pd.Timestamp("2012-08-31 00:00:00"),
            122: pd.Timestamp("2012-09-01 00:00:00"),
            123: pd.Timestamp("2012-09-02 00:00:00"),
            124: pd.Timestamp("2012-09-03 00:00:00"),
            125: pd.Timestamp("2012-09-04 00:00:00"),
            126: pd.Timestamp("2012-09-05 00:00:00"),
            127: pd.Timestamp("2012-09-06 00:00:00"),
            128: pd.Timestamp("2012-09-07 00:00:00"),
            129: pd.Timestamp("2012-09-08 00:00:00"),
            130: pd.Timestamp("2012-09-09 00:00:00"),
            131: pd.Timestamp("2012-09-10 00:00:00"),
            132: pd.Timestamp("2012-09-11 00:00:00"),
            133: pd.Timestamp("2012-09-12 00:00:00"),
            134: pd.Timestamp("2012-09-13 00:00:00"),
            135: pd.Timestamp("2012-09-14 00:00:00"),
            136: pd.Timestamp("2012-09-15 00:00:00"),
            137: pd.Timestamp("2012-09-16 00:00:00"),
            138: pd.Timestamp("2012-09-17 00:00:00"),
            139: pd.Timestamp("2012-09-18 00:00:00"),
            140: pd.Timestamp("2012-09-19 00:00:00"),
            141: pd.Timestamp("2012-09-20 00:00:00"),
            142: pd.Timestamp("2012-09-21 00:00:00"),
            143: pd.Timestamp("2012-09-22 00:00:00"),
            144: pd.Timestamp("2012-09-23 00:00:00"),
            145: pd.Timestamp("2012-09-24 00:00:00"),
            146: pd.Timestamp("2012-09-25 00:00:00"),
            147: pd.Timestamp("2012-09-26 00:00:00"),
            148: pd.Timestamp("2012-09-27 00:00:00"),
            149: pd.Timestamp("2012-09-28 00:00:00"),
            150: pd.Timestamp("2012-09-29 00:00:00"),
            151: pd.Timestamp("2012-09-30 00:00:00"),
            152: pd.Timestamp("2012-10-01 00:00:00"),
            153: pd.Timestamp("2012-10-02 00:00:00"),
            154: pd.Timestamp("2012-10-03 00:00:00"),
            155: pd.Timestamp("2012-10-04 00:00:00"),
            156: pd.Timestamp("2012-10-05 00:00:00"),
            157: pd.Timestamp("2012-10-06 00:00:00"),
            158: pd.Timestamp("2012-10-07 00:00:00"),
            159: pd.Timestamp("2012-10-08 00:00:00"),
            160: pd.Timestamp("2012-10-09 00:00:00"),
            161: pd.Timestamp("2012-10-10 00:00:00"),
            162: pd.Timestamp("2012-10-11 00:00:00"),
            163: pd.Timestamp("2012-10-12 00:00:00"),
            164: pd.Timestamp("2012-10-13 00:00:00"),
            165: pd.Timestamp("2012-10-14 00:00:00"),
            166: pd.Timestamp("2012-10-15 00:00:00"),
            167: pd.Timestamp("2012-10-16 00:00:00"),
            168: pd.Timestamp("2012-10-17 00:00:00"),
            169: pd.Timestamp("2012-10-18 00:00:00"),
            170: pd.Timestamp("2012-10-19 00:00:00"),
            171: pd.Timestamp("2012-10-20 00:00:00"),
            172: pd.Timestamp("2012-10-21 00:00:00"),
            173: pd.Timestamp("2012-10-22 00:00:00"),
            174: pd.Timestamp("2012-10-23 00:00:00"),
            175: pd.Timestamp("2012-10-24 00:00:00"),
            176: pd.Timestamp("2012-10-25 00:00:00"),
            177: pd.Timestamp("2012-10-26 00:00:00"),
            178: pd.Timestamp("2012-10-27 00:00:00"),
            179: pd.Timestamp("2012-10-28 00:00:00"),
            180: pd.Timestamp("2012-10-29 00:00:00"),
            181: pd.Timestamp("2012-10-30 00:00:00"),
            182: pd.Timestamp("2012-10-31 00:00:00"),
            183: pd.Timestamp("2012-11-01 00:00:00"),
            184: pd.Timestamp("2012-11-02 00:00:00"),
            185: pd.Timestamp("2012-11-03 00:00:00"),
            186: pd.Timestamp("2012-11-04 00:00:00"),
            187: pd.Timestamp("2012-11-05 00:00:00"),
            188: pd.Timestamp("2012-11-06 00:00:00"),
            189: pd.Timestamp("2012-11-07 00:00:00"),
            190: pd.Timestamp("2012-11-08 00:00:00"),
            191: pd.Timestamp("2012-11-09 00:00:00"),
            192: pd.Timestamp("2012-11-10 00:00:00"),
            193: pd.Timestamp("2012-11-11 00:00:00"),
            194: pd.Timestamp("2012-11-12 00:00:00"),
            195: pd.Timestamp("2012-11-13 00:00:00"),
            196: pd.Timestamp("2012-11-14 00:00:00"),
            197: pd.Timestamp("2012-11-15 00:00:00"),
            198: pd.Timestamp("2012-11-16 00:00:00"),
            199: pd.Timestamp("2012-11-17 00:00:00"),
            200: pd.Timestamp("2012-11-18 00:00:00"),
            201: pd.Timestamp("2012-11-19 00:00:00"),
            202: pd.Timestamp("2012-11-20 00:00:00"),
            203: pd.Timestamp("2012-11-21 00:00:00"),
            204: pd.Timestamp("2012-11-22 00:00:00"),
            205: pd.Timestamp("2012-11-23 00:00:00"),
            206: pd.Timestamp("2012-11-24 00:00:00"),
            207: pd.Timestamp("2012-11-25 00:00:00"),
            208: pd.Timestamp("2012-11-26 00:00:00"),
            209: pd.Timestamp("2012-11-27 00:00:00"),
            210: pd.Timestamp("2012-11-28 00:00:00"),
            211: pd.Timestamp("2012-11-29 00:00:00"),
            212: pd.Timestamp("2012-11-30 00:00:00"),
            213: pd.Timestamp("2012-12-01 00:00:00"),
            214: pd.Timestamp("2012-12-02 00:00:00"),
            215: pd.Timestamp("2012-12-03 00:00:00"),
            216: pd.Timestamp("2012-12-04 00:00:00"),
            217: pd.Timestamp("2012-12-05 00:00:00"),
            218: pd.Timestamp("2012-12-06 00:00:00"),
            219: pd.Timestamp("2012-12-07 00:00:00"),
            220: pd.Timestamp("2012-12-08 00:00:00"),
            221: pd.Timestamp("2012-12-09 00:00:00"),
            222: pd.Timestamp("2012-12-10 00:00:00"),
            223: pd.Timestamp("2012-12-11 00:00:00"),
            224: pd.Timestamp("2012-12-12 00:00:00"),
            225: pd.Timestamp("2012-12-13 00:00:00"),
            226: pd.Timestamp("2012-12-14 00:00:00"),
            227: pd.Timestamp("2012-12-15 00:00:00"),
            228: pd.Timestamp("2012-12-16 00:00:00"),
            229: pd.Timestamp("2012-12-17 00:00:00"),
            230: pd.Timestamp("2012-12-18 00:00:00"),
            231: pd.Timestamp("2012-12-19 00:00:00"),
            232: pd.Timestamp("2012-12-20 00:00:00"),
            233: pd.Timestamp("2012-12-21 00:00:00"),
            234: pd.Timestamp("2012-12-22 00:00:00"),
            235: pd.Timestamp("2012-12-23 00:00:00"),
            236: pd.Timestamp("2012-12-24 00:00:00"),
            237: pd.Timestamp("2012-12-25 00:00:00"),
            238: pd.Timestamp("2012-12-26 00:00:00"),
            239: pd.Timestamp("2012-12-27 00:00:00"),
            240: pd.Timestamp("2012-12-28 00:00:00"),
            241: pd.Timestamp("2012-12-29 00:00:00"),
            242: pd.Timestamp("2012-12-30 00:00:00"),
            243: pd.Timestamp("2012-12-31 00:00:00"),
            244: pd.Timestamp("2013-01-01 00:00:00"),
            245: pd.Timestamp("2013-01-02 00:00:00"),
            246: pd.Timestamp("2013-01-03 00:00:00"),
            247: pd.Timestamp("2013-01-04 00:00:00"),
            248: pd.Timestamp("2013-01-05 00:00:00"),
            249: pd.Timestamp("2013-01-06 00:00:00"),
            250: pd.Timestamp("2013-01-07 00:00:00"),
            251: pd.Timestamp("2013-01-08 00:00:00"),
            252: pd.Timestamp("2013-01-09 00:00:00"),
            253: pd.Timestamp("2013-01-10 00:00:00"),
            254: pd.Timestamp("2013-01-11 00:00:00"),
            255: pd.Timestamp("2013-01-12 00:00:00"),
            256: pd.Timestamp("2013-01-13 00:00:00"),
            257: pd.Timestamp("2013-01-14 00:00:00"),
            258: pd.Timestamp("2013-01-15 00:00:00"),
            259: pd.Timestamp("2013-01-16 00:00:00"),
            260: pd.Timestamp("2013-01-17 00:00:00"),
            261: pd.Timestamp("2013-01-18 00:00:00"),
            262: pd.Timestamp("2013-01-19 00:00:00"),
            263: pd.Timestamp("2013-01-20 00:00:00"),
            264: pd.Timestamp("2013-01-21 00:00:00"),
            265: pd.Timestamp("2013-01-22 00:00:00"),
            266: pd.Timestamp("2013-01-23 00:00:00"),
            267: pd.Timestamp("2013-01-24 00:00:00"),
            268: pd.Timestamp("2013-01-25 00:00:00"),
            269: pd.Timestamp("2013-01-26 00:00:00"),
            270: pd.Timestamp("2013-01-27 00:00:00"),
            271: pd.Timestamp("2013-01-28 00:00:00"),
            272: pd.Timestamp("2013-01-29 00:00:00"),
            273: pd.Timestamp("2013-01-30 00:00:00"),
            274: pd.Timestamp("2013-01-31 00:00:00"),
            275: pd.Timestamp("2013-02-01 00:00:00"),
            276: pd.Timestamp("2013-02-02 00:00:00"),
            277: pd.Timestamp("2013-02-03 00:00:00"),
            278: pd.Timestamp("2013-02-04 00:00:00"),
            279: pd.Timestamp("2013-02-05 00:00:00"),
            280: pd.Timestamp("2013-02-06 00:00:00"),
            281: pd.Timestamp("2013-02-07 00:00:00"),
            282: pd.Timestamp("2013-02-08 00:00:00"),
            283: pd.Timestamp("2013-02-09 00:00:00"),
            284: pd.Timestamp("2013-02-10 00:00:00"),
            285: pd.Timestamp("2013-02-11 00:00:00"),
            286: pd.Timestamp("2013-02-12 00:00:00"),
            287: pd.Timestamp("2013-02-13 00:00:00"),
            288: pd.Timestamp("2013-02-14 00:00:00"),
            289: pd.Timestamp("2013-02-15 00:00:00"),
            290: pd.Timestamp("2013-02-16 00:00:00"),
            291: pd.Timestamp("2013-02-17 00:00:00"),
            292: pd.Timestamp("2013-02-18 00:00:00"),
            293: pd.Timestamp("2013-02-19 00:00:00"),
            294: pd.Timestamp("2013-02-20 00:00:00"),
            295: pd.Timestamp("2013-02-21 00:00:00"),
            296: pd.Timestamp("2013-02-22 00:00:00"),
            297: pd.Timestamp("2013-02-23 00:00:00"),
            298: pd.Timestamp("2013-02-24 00:00:00"),
            299: pd.Timestamp("2013-02-25 00:00:00"),
            300: pd.Timestamp("2013-02-26 00:00:00"),
            301: pd.Timestamp("2013-02-27 00:00:00"),
            302: pd.Timestamp("2013-02-28 00:00:00"),
            303: pd.Timestamp("2013-03-01 00:00:00"),
            304: pd.Timestamp("2013-03-02 00:00:00"),
            305: pd.Timestamp("2013-03-03 00:00:00"),
            306: pd.Timestamp("2013-03-04 00:00:00"),
            307: pd.Timestamp("2013-03-05 00:00:00"),
            308: pd.Timestamp("2013-03-06 00:00:00"),
            309: pd.Timestamp("2013-03-07 00:00:00"),
            310: pd.Timestamp("2013-03-08 00:00:00"),
            311: pd.Timestamp("2013-03-09 00:00:00"),
            312: pd.Timestamp("2013-03-10 00:00:00"),
            313: pd.Timestamp("2013-03-11 00:00:00"),
            314: pd.Timestamp("2013-03-12 00:00:00"),
            315: pd.Timestamp("2013-03-13 00:00:00"),
            316: pd.Timestamp("2013-03-14 00:00:00"),
            317: pd.Timestamp("2013-03-15 00:00:00"),
            318: pd.Timestamp("2013-03-16 00:00:00"),
            319: pd.Timestamp("2013-03-17 00:00:00"),
            320: pd.Timestamp("2013-03-18 00:00:00"),
            321: pd.Timestamp("2013-03-19 00:00:00"),
            322: pd.Timestamp("2013-03-20 00:00:00"),
            323: pd.Timestamp("2013-03-21 00:00:00"),
            324: pd.Timestamp("2013-03-22 00:00:00"),
            325: pd.Timestamp("2013-03-23 00:00:00"),
            326: pd.Timestamp("2013-03-24 00:00:00"),
            327: pd.Timestamp("2013-03-25 00:00:00"),
            328: pd.Timestamp("2013-03-26 00:00:00"),
            329: pd.Timestamp("2013-03-27 00:00:00"),
            330: pd.Timestamp("2013-03-28 00:00:00"),
            331: pd.Timestamp("2013-03-29 00:00:00"),
            332: pd.Timestamp("2013-03-30 00:00:00"),
            333: pd.Timestamp("2013-03-31 00:00:00"),
            334: pd.Timestamp("2013-04-01 00:00:00"),
            335: pd.Timestamp("2013-04-02 00:00:00"),
            336: pd.Timestamp("2013-04-03 00:00:00"),
            337: pd.Timestamp("2013-04-04 00:00:00"),
            338: pd.Timestamp("2013-04-05 00:00:00"),
            339: pd.Timestamp("2013-04-06 00:00:00"),
            340: pd.Timestamp("2013-04-07 00:00:00"),
            341: pd.Timestamp("2013-04-08 00:00:00"),
            342: pd.Timestamp("2013-04-09 00:00:00"),
            343: pd.Timestamp("2013-04-10 00:00:00"),
            344: pd.Timestamp("2013-04-11 00:00:00"),
            345: pd.Timestamp("2013-04-12 00:00:00"),
            346: pd.Timestamp("2013-04-13 00:00:00"),
            347: pd.Timestamp("2013-04-14 00:00:00"),
            348: pd.Timestamp("2013-04-15 00:00:00"),
            349: pd.Timestamp("2013-04-16 00:00:00"),
            350: pd.Timestamp("2013-04-17 00:00:00"),
            351: pd.Timestamp("2013-04-18 00:00:00"),
            352: pd.Timestamp("2013-04-19 00:00:00"),
            353: pd.Timestamp("2013-04-20 00:00:00"),
            354: pd.Timestamp("2013-04-21 00:00:00"),
            355: pd.Timestamp("2013-04-22 00:00:00"),
            356: pd.Timestamp("2013-04-23 00:00:00"),
            357: pd.Timestamp("2013-04-24 00:00:00"),
            358: pd.Timestamp("2013-04-25 00:00:00"),
            359: pd.Timestamp("2013-04-26 00:00:00"),
            360: pd.Timestamp("2013-04-27 00:00:00"),
            361: pd.Timestamp("2013-04-28 00:00:00"),
            362: pd.Timestamp("2013-04-29 00:00:00"),
            363: pd.Timestamp("2013-04-30 00:00:00"),
            364: pd.Timestamp("2013-05-01 00:00:00"),
            365: pd.Timestamp("2013-05-02 00:00:00"),
            366: pd.Timestamp("2013-05-03 00:00:00"),
            367: pd.Timestamp("2013-05-04 00:00:00"),
            368: pd.Timestamp("2013-05-05 00:00:00"),
            369: pd.Timestamp("2013-05-06 00:00:00"),
            370: pd.Timestamp("2013-05-07 00:00:00"),
            371: pd.Timestamp("2013-05-08 00:00:00"),
            372: pd.Timestamp("2013-05-09 00:00:00"),
            373: pd.Timestamp("2013-05-10 00:00:00"),
            374: pd.Timestamp("2013-05-11 00:00:00"),
            375: pd.Timestamp("2013-05-12 00:00:00"),
            376: pd.Timestamp("2013-05-13 00:00:00"),
            377: pd.Timestamp("2013-05-14 00:00:00"),
            378: pd.Timestamp("2013-05-15 00:00:00"),
        },
        "fcst": {
            0: 7.710195925566869,
            1: 7.744907999726215,
            2: 7.7926437941918705,
            3: 7.624593397388267,
            4: 8.086088963498527,
            5: 8.226938714098187,
            6: 7.936541087327886,
            7: 7.709339119187576,
            8: 7.744051193346707,
            9: 7.791786987811981,
            10: 7.623736591008531,
            11: 8.08523215711923,
            12: 8.226081907718582,
            13: 7.935684280956517,
            14: 7.708482312822571,
            15: 7.743194386989579,
            16: 7.790930181462328,
            17: 7.622879784666338,
            18: 8.084375350783924,
            19: 8.22522510139134,
            20: 7.934827474629642,
            21: 7.707625506495359,
            22: 7.742337580662688,
            23: 7.7900733751356706,
            24: 7.622022979465374,
            25: 8.08351854670914,
            26: 8.22436829844187,
            27: 7.933970672805973,
            28: 7.706768705797579,
            29: 7.741480781090415,
            30: 7.7892165766891654,
            31: 7.621166181019212,
            32: 8.082661748263359,
            33: 8.2235114999956,
            34: 7.9331138743601315,
            35: 7.705911907351373,
            36: 7.740937906482861,
            37: 7.788987625920119,
            38: 7.6212511540883074,
            39: 8.083060645169969,
            40: 8.224224320741705,
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        },
    }
)

AIR_FCST_15_PROPHET_LOGISTIC_CAP_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
        },
        "fcst": {
            0: 474.8362799512042,
            1: 468.9916881899657,
            2: 502.0106903871963,
            3: 501.49959017106704,
            4: 505.79992157649934,
            5: 547.8462762836779,
            6: 587.6968530468348,
            7: 588.3403217412557,
            8: 539.7408809101208,
            9: 505.7051154784511,
            10: 471.88305612695933,
            11: 501.7918421053647,
            12: 514.9356960790084,
            13: 509.0208125734815,
            14: 545.3487965572431,
        },
        "fcst_lower": {
            0: 445.0823998560953,
            1: 441.72749406446144,
            2: 470.5568868447796,
            3: 474.05250855995007,
            4: 478.23211749758195,
            5: 518.8269948517109,
            6: 559.1549826080967,
            7: 559.2979790265297,
            8: 511.69338424718364,
            9: 480.81903923309454,
            10: 442.6199454492793,
            11: 473.5805909279045,
            12: 484.3658924969916,
            13: 479.81756865254147,
            14: 517.8966627852753,
        },
        "fcst_upper": {
            0: 503.8120428388288,
            1: 498.34230267364455,
            2: 530.9444925914046,
            3: 532.0118708692428,
            4: 532.523958222772,
            5: 576.0691183255308,
            6: 617.4051079222874,
            7: 617.1023278205932,
            8: 567.7339463033223,
            9: 534.7247116394298,
            10: 499.7378203060462,
            11: 529.0246221670866,
            12: 543.5705260770047,
            13: 537.5926543535654,
            14: 574.6001147007677,
        },
    }
)

PEYTON_FCST_30_PROPHET_DAILY_CAP_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.331859311253365,
            1: 7.354249349679736,
            2: 7.389757711441401,
            3: 7.209493662824087,
            4: 7.658651320662986,
            5: 7.7872419710607055,
            6: 7.484632036296666,
            7: 7.248539363366209,
            8: 7.270983523103537,
            9: 7.306546222992052,
            10: 7.126336728682114,
            11: 7.575549156371828,
            12: 7.704194791527012,
            13: 7.40164005578452,
            14: 7.165602795499072,
            15: 7.188102580860686,
            16: 7.223721118706291,
            17: 7.043567674038666,
            18: 7.492836362407444,
            19: 7.621538468625771,
            20: 7.3190404136790495,
            21: 7.083060043267397,
            22: 7.105616926923051,
            23: 7.14129277082636,
            24: 6.961196839320299,
            25: 7.410523247291657,
            26: 7.539283278893468,
            27: 7.236843354444335,
            28: 7.000921318978118,
            29: 7.0235367413603775,
        },
        "fcst_lower": {
            0: 6.6842439270652205,
            1: 6.713058005213148,
            2: 6.754440320348967,
            3: 6.606527358631897,
            4: 7.044280714471815,
            5: 7.115121374747295,
            6: 6.9040178657359474,
            7: 6.636407608070906,
            8: 6.636050657550367,
            9: 6.692957917957333,
            10: 6.5206113819888545,
            11: 6.9775267723980035,
            12: 7.071121866716252,
            13: 6.760760489988774,
            14: 6.542062061397551,
            15: 6.616282717620708,
            16: 6.6470865621378294,
            17: 6.371323918061791,
            18: 6.906073772303484,
            19: 7.029498158978105,
            20: 6.685922550590015,
            21: 6.4580979731311094,
            22: 6.482813339162121,
            23: 6.504087835171158,
            24: 6.382263944707629,
            25: 6.819695194686108,
            26: 6.914474389209892,
            27: 6.563884948007971,
            28: 6.359884556154696,
            29: 6.494487552730167,
        },
        "fcst_upper": {
            0: 7.949249097823939,
            1: 7.93438072107086,
            2: 8.015571324958122,
            3: 7.865807072341612,
            4: 8.29772148729878,
            5: 8.39031213596704,
            6: 8.0905365450609,
            7: 7.85894033719714,
            8: 7.842451969526193,
            9: 7.8885555797463605,
            10: 7.746548997008269,
            11: 8.152878189668238,
            12: 8.256303000560008,
            13: 7.992823490079385,
            14: 7.779979178951879,
            15: 7.78162044235521,
            16: 7.850448062145312,
            17: 7.638227447522366,
            18: 8.103666926540505,
            19: 8.2112302359874,
            20: 7.903303177997009,
            21: 7.6553686926351245,
            22: 7.712960422945617,
            23: 7.757893199604111,
            24: 7.578836431938248,
            25: 8.035622771717362,
            26: 8.157204546960445,
            27: 7.844948363142048,
            28: 7.599181867880122,
            29: 7.621823993259122,
        },
    }
)

AIR_FCST_30_PROPHET_CUSTOM_SEASONALITY_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1961-01-01 00:00:00"),
            1: pd.Timestamp("1961-02-01 00:00:00"),
            2: pd.Timestamp("1961-03-01 00:00:00"),
            3: pd.Timestamp("1961-04-01 00:00:00"),
            4: pd.Timestamp("1961-05-01 00:00:00"),
            5: pd.Timestamp("1961-06-01 00:00:00"),
            6: pd.Timestamp("1961-07-01 00:00:00"),
            7: pd.Timestamp("1961-08-01 00:00:00"),
            8: pd.Timestamp("1961-09-01 00:00:00"),
            9: pd.Timestamp("1961-10-01 00:00:00"),
            10: pd.Timestamp("1961-11-01 00:00:00"),
            11: pd.Timestamp("1961-12-01 00:00:00"),
            12: pd.Timestamp("1962-01-01 00:00:00"),
            13: pd.Timestamp("1962-02-01 00:00:00"),
            14: pd.Timestamp("1962-03-01 00:00:00"),
            15: pd.Timestamp("1962-04-01 00:00:00"),
            16: pd.Timestamp("1962-05-01 00:00:00"),
            17: pd.Timestamp("1962-06-01 00:00:00"),
            18: pd.Timestamp("1962-07-01 00:00:00"),
            19: pd.Timestamp("1962-08-01 00:00:00"),
            20: pd.Timestamp("1962-09-01 00:00:00"),
            21: pd.Timestamp("1962-10-01 00:00:00"),
            22: pd.Timestamp("1962-11-01 00:00:00"),
            23: pd.Timestamp("1962-12-01 00:00:00"),
            24: pd.Timestamp("1963-01-01 00:00:00"),
            25: pd.Timestamp("1963-02-01 00:00:00"),
            26: pd.Timestamp("1963-03-01 00:00:00"),
            27: pd.Timestamp("1963-04-01 00:00:00"),
            28: pd.Timestamp("1963-05-01 00:00:00"),
            29: pd.Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 452.88345517479746,
            1: 437.9750764863489,
            2: 564.0581722096811,
            3: 532.4626894414829,
            4: 565.4929535283603,
            5: 576.5925608005873,
            6: 645.167708008004,
            7: 615.5653243735584,
            8: 541.8783558909014,
            9: 531.4149061053702,
            10: 472.5774066716521,
            11: 526.3906583847821,
            12: 510.13331047183823,
            13: 483.1039140043139,
            14: 664.434422175725,
            15: 628.340440837633,
            16: 666.2631672975554,
            17: 673.4054958880696,
            18: 745.6452388016862,
            19: 713.3207318286643,
            20: 630.0995226187222,
            21: 628.5221175410493,
            22: 560.2062003332692,
            23: 623.9829608974794,
            24: 597.3003973985731,
            25: 556.1991961776957,
            26: 738.4514474984936,
            27: 718.6025531101388,
            28: 740.7349265395878,
            29: 764.6053723392605,
        },
        "fcst_lower": {
            0: 424.682921950405,
            1: 412.5679900824412,
            2: 535.9761313681635,
            3: 504.12183621258754,
            4: 538.4321389211508,
            5: 546.6907787357392,
            6: 618.8095221467094,
            7: 589.9973683308615,
            8: 517.1393430322438,
            9: 505.3623630365468,
            10: 444.84252772723704,
            11: 498.8958861672086,
            12: 483.1574351204671,
            13: 456.3880288790717,
            14: 636.4381386505919,
            15: 602.2841171281649,
            16: 638.8671049496324,
            17: 646.4307940648474,
            18: 718.048688328434,
            19: 685.8153320933951,
            20: 604.2678273379327,
            21: 599.6899383865052,
            22: 532.942733182364,
            23: 595.8332727537743,
            24: 571.9841531156071,
            25: 526.8099646425002,
            26: 713.66525562444,
            27: 689.9773942342617,
            28: 714.9313974529585,
            29: 738.3841244059056,
        },
        "fcst_upper": {
            0: 479.15424546477635,
            1: 464.0312190445665,
            2: 590.4750819576508,
            3: 559.1397365356636,
            4: 591.4381219255607,
            5: 603.8557390466535,
            6: 672.5638319850465,
            7: 644.6682785486421,
            8: 569.5706684196123,
            9: 558.9295999343411,
            10: 498.08943585149916,
            11: 553.1515454929595,
            12: 537.1373508202025,
            13: 509.16408199479247,
            14: 690.3431234462456,
            15: 655.8400715120379,
            16: 690.8885483160265,
            17: 700.3747360683956,
            18: 772.4038012876088,
            19: 741.545426591666,
            20: 656.433095984407,
            21: 656.5497802841992,
            22: 587.2811180341304,
            23: 650.6050821785003,
            24: 623.055539348631,
            25: 582.7958231656721,
            26: 765.648745406405,
            27: 744.6580380710474,
            28: 770.1133464429854,
            29: 791.3905219842743,
        },
    }
)

PEYTON_FCST_30_PROPHET_CUSTOM_SEASONALITY_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2013-05-01 00:00:00"),
            1: pd.Timestamp("2013-05-02 00:00:00"),
            2: pd.Timestamp("2013-05-03 00:00:00"),
            3: pd.Timestamp("2013-05-04 00:00:00"),
            4: pd.Timestamp("2013-05-05 00:00:00"),
            5: pd.Timestamp("2013-05-06 00:00:00"),
            6: pd.Timestamp("2013-05-07 00:00:00"),
            7: pd.Timestamp("2013-05-08 00:00:00"),
            8: pd.Timestamp("2013-05-09 00:00:00"),
            9: pd.Timestamp("2013-05-10 00:00:00"),
            10: pd.Timestamp("2013-05-11 00:00:00"),
            11: pd.Timestamp("2013-05-12 00:00:00"),
            12: pd.Timestamp("2013-05-13 00:00:00"),
            13: pd.Timestamp("2013-05-14 00:00:00"),
            14: pd.Timestamp("2013-05-15 00:00:00"),
            15: pd.Timestamp("2013-05-16 00:00:00"),
            16: pd.Timestamp("2013-05-17 00:00:00"),
            17: pd.Timestamp("2013-05-18 00:00:00"),
            18: pd.Timestamp("2013-05-19 00:00:00"),
            19: pd.Timestamp("2013-05-20 00:00:00"),
            20: pd.Timestamp("2013-05-21 00:00:00"),
            21: pd.Timestamp("2013-05-22 00:00:00"),
            22: pd.Timestamp("2013-05-23 00:00:00"),
            23: pd.Timestamp("2013-05-24 00:00:00"),
            24: pd.Timestamp("2013-05-25 00:00:00"),
            25: pd.Timestamp("2013-05-26 00:00:00"),
            26: pd.Timestamp("2013-05-27 00:00:00"),
            27: pd.Timestamp("2013-05-28 00:00:00"),
            28: pd.Timestamp("2013-05-29 00:00:00"),
            29: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.3037491155836,
            1: 7.298802654989095,
            2: 7.302759950741621,
            3: 7.087397229569874,
            4: 7.498713130599085,
            5: 7.587266579175969,
            6: 7.243228524765057,
            7: 6.957122764380708,
            8: 6.937961394872215,
            9: 6.932939334396985,
            10: 6.713982929830124,
            11: 7.127100370274706,
            12: 7.222720303593862,
            13: 6.890748090874125,
            14: 6.621314782411355,
            15: 6.622924120496868,
            16: 6.642161678096348,
            17: 6.450260735153485,
            18: 6.892472125750261,
            19: 7.0184239080258815,
            20: 6.717199704101642,
            21: 6.478110179851799,
            22: 6.508862347295869,
            23: 6.555290184879691,
            24: 6.387940589397616,
            25: 6.85146134706936,
            26: 6.994976487299352,
            27: 6.707177448764311,
            28: 6.477104192294041,
            29: 6.512320699071476,
        },
        "fcst_lower": {
            0: 6.717252779036359,
            1: 6.718124085984931,
            2: 6.727356960944083,
            3: 6.540505402788345,
            4: 6.9423240783761555,
            5: 6.978577722980976,
            6: 6.717895242425473,
            7: 6.399576409383779,
            8: 6.368149007248556,
            9: 6.382128615992393,
            10: 6.165423236124337,
            11: 6.585560928300749,
            12: 6.648173270105328,
            13: 6.310032220065989,
            14: 6.054488305589597,
            15: 6.103721169443442,
            16: 6.124426526327651,
            17: 5.83892418242846,
            18: 6.362965901550441,
            19: 6.478591082020587,
            20: 6.140482234300091,
            21: 5.910310146894073,
            22: 5.94163876550658,
            23: 5.9741522761438635,
            24: 5.854476398076014,
            25: 6.317910733216211,
            26: 6.426611998730378,
            27: 6.091164986077031,
            28: 5.883013233762382,
            29: 6.030362117789187,
        },
        "fcst_upper": {
            0: 7.862872411425703,
            1: 7.824183818476362,
            2: 7.869512070364395,
            3: 7.681770710662209,
            4: 8.077548105946294,
            5: 8.133421675293558,
            6: 7.793681230930931,
            7: 7.510239664570255,
            8: 7.4565809990786605,
            9: 7.463485876121115,
            10: 7.274334203168064,
            11: 7.654608992969765,
            12: 7.723338203519616,
            13: 7.426595502999383,
            14: 7.179817840416072,
            15: 7.155976160949455,
            16: 7.210038382027373,
            17: 6.985326382781782,
            18: 7.439788720419854,
            19: 7.5553759309243445,
            20: 7.246882224102252,
            21: 6.997163599111351,
            22: 7.056755226508162,
            23: 7.112151947124181,
            24: 6.949637505738485,
            25: 7.418501055847267,
            26: 7.558483941708863,
            27: 7.263961758272869,
            28: 7.023147231669527,
            29: 7.055461672474383,
        },
    }
)

NONSEASONAL_FCST_15_PROPHET_ARG_FUTURE_SM_12 = pd.DataFrame(
    {
        "ds": {
            0: pd.Timestamp("1963-01-31 00:00:00"),
            1: pd.Timestamp("1963-02-28 00:00:00"),
            2: pd.Timestamp("1963-03-31 00:00:00"),
            3: pd.Timestamp("1963-04-30 00:00:00"),
            4: pd.Timestamp("1963-05-31 00:00:00"),
            5: pd.Timestamp("1963-06-30 00:00:00"),
            6: pd.Timestamp("1963-07-31 00:00:00"),
            7: pd.Timestamp("1963-08-31 00:00:00"),
            8: pd.Timestamp("1963-09-30 00:00:00"),
            9: pd.Timestamp("1963-10-31 00:00:00"),
            10: pd.Timestamp("1963-11-30 00:00:00"),
            11: pd.Timestamp("1963-12-31 00:00:00"),
        },
        "trend": {
            0: 1.486350603534519,
            1: 1.5153722851275742,
            2: 1.5475034326055999,
            3: 1.578598091455302,
            4: 1.6107292389333274,
            5: 1.6418238977830293,
            6: 1.6739550452610548,
            7: 1.7060861927390802,
            8: 1.7371808515887825,
            9: 1.7693119990668078,
            10: 1.8004066579165097,
            11: 1.8325378053945354,
        },
        "yhat_lower": {
            0: 1.1594374527896225,
            1: -2.248335089418763,
            2: 2.0604229200339033,
            3: 2.282279023692054,
            4: 1.2809544896920573,
            5: -0.3992332220290529,
            6: -3.552547372716627,
            7: 2.4299554266978927,
            8: 2.90002363984548,
            9: 0.697405496110251,
            10: 0.9017089206367225,
            11: 0.953747778118578,
        },
        "yhat_upper": {
            0: 1.6201165163539932,
            1: -1.7455747337333132,
            2: 2.5318091002522354,
            3: 2.7779595054263746,
            4: 1.7376132983822454,
            5: 0.07946819642562761,
            6: -3.0853192468484716,
            7: 2.896084899140792,
            8: 3.3712332482598972,
            9: 1.1821260937977756,
            10: 1.3997416814214965,
            11: 1.44748259249655,
        },
        "trend_lower": {
            0: 1.4863505500576313,
            1: 1.5153720936426587,
            2: 1.5475030409697264,
            3: 1.5785974767860411,
            4: 1.6107283337518905,
            5: 1.6418227060060375,
            6: 1.6739535322013472,
            7: 1.706084373854531,
            8: 1.737178641074902,
            9: 1.7693093386754053,
            10: 1.8004035415059678,
            11: 1.832534164518234,
        },
        "trend_upper": {
            0: 1.4863506599270937,
            1: 1.5153724517235998,
            2: 1.5475037804678078,
            3: 1.5785986386243034,
            4: 1.6107300179963508,
            5: 1.6418249316723945,
            6: 1.673956380607812,
            7: 1.7060878674661635,
            8: 1.7371828907559823,
            9: 1.76931438969385,
            10: 1.8004094470881942,
            11: 1.8325410022081747,
        },
        "additive_terms": {
            0: -0.10603153621962905,
            1: -3.5100601864407293,
            2: 0.7525595383687024,
            3: 0.949597753337762,
            4: -0.08872196007635745,
            5: -1.789757575277333,
            6: -4.992084112210709,
            7: 0.943992044598797,
            8: 1.4107642203091333,
            9: -0.8269897561986163,
            10: -0.633279481381239,
            11: -0.6277870965254754,
        },
        "additive_terms_lower": {
            0: -0.10603153621962905,
            1: -3.5100601864407293,
            2: 0.7525595383687024,
            3: 0.949597753337762,
            4: -0.08872196007635745,
            5: -1.789757575277333,
            6: -4.992084112210709,
            7: 0.943992044598797,
            8: 1.4107642203091333,
            9: -0.8269897561986163,
            10: -0.633279481381239,
            11: -0.6277870965254754,
        },
        "additive_terms_upper": {
            0: -0.10603153621962905,
            1: -3.5100601864407293,
            2: 0.7525595383687024,
            3: 0.949597753337762,
            4: -0.08872196007635745,
            5: -1.789757575277333,
            6: -4.992084112210709,
            7: 0.943992044598797,
            8: 1.4107642203091333,
            9: -0.8269897561986163,
            10: -0.633279481381239,
            11: -0.6277870965254754,
        },
        "yearly": {
            0: -0.10603153621962905,
            1: -3.5100601864407293,
            2: 0.7525595383687024,
            3: 0.949597753337762,
            4: -0.08872196007635745,
            5: -1.789757575277333,
            6: -4.992084112210709,
            7: 0.943992044598797,
            8: 1.4107642203091333,
            9: -0.8269897561986163,
            10: -0.633279481381239,
            11: -0.6277870965254754,
        },
        "yearly_lower": {
            0: -0.10603153621962905,
            1: -3.5100601864407293,
            2: 0.7525595383687024,
            3: 0.949597753337762,
            4: -0.08872196007635745,
            5: -1.789757575277333,
            6: -4.992084112210709,
            7: 0.943992044598797,
            8: 1.4107642203091333,
            9: -0.8269897561986163,
            10: -0.633279481381239,
            11: -0.6277870965254754,
        },
        "yearly_upper": {
            0: -0.10603153621962905,
            1: -3.5100601864407293,
            2: 0.7525595383687024,
            3: 0.949597753337762,
            4: -0.08872196007635745,
            5: -1.789757575277333,
            6: -4.992084112210709,
            7: 0.943992044598797,
            8: 1.4107642203091333,
            9: -0.8269897561986163,
            10: -0.633279481381239,
            11: -0.6277870965254754,
        },
        "multiplicative_terms": {
            0: 0.0,
            1: 0.0,
            2: 0.0,
            3: 0.0,
            4: 0.0,
            5: 0.0,
            6: 0.0,
            7: 0.0,
            8: 0.0,
            9: 0.0,
            10: 0.0,
            11: 0.0,
        },
        "multiplicative_terms_lower": {
            0: 0.0,
            1: 0.0,
            2: 0.0,
            3: 0.0,
            4: 0.0,
            5: 0.0,
            6: 0.0,
            7: 0.0,
            8: 0.0,
            9: 0.0,
            10: 0.0,
            11: 0.0,
        },
        "multiplicative_terms_upper": {
            0: 0.0,
            1: 0.0,
            2: 0.0,
            3: 0.0,
            4: 0.0,
            5: 0.0,
            6: 0.0,
            7: 0.0,
            8: 0.0,
            9: 0.0,
            10: 0.0,
            11: 0.0,
        },
        "yhat": {
            0: 1.38031906731489,
            1: -1.994687901313155,
            2: 2.3000629709743023,
            3: 2.528195844793064,
            4: 1.52200727885697,
            5: -0.14793367749430364,
            6: -3.3181290669496546,
            7: 2.650078237337877,
            8: 3.147945071897916,
            9: 0.9423222428681916,
            10: 1.1671271765352706,
            11: 1.20475070886906,
        },
    }
)

AIR_FCST_15_THETA_SM_11 = pd.DataFrame(
    {
        "time": {
            144: pd.Timestamp("1961-01-01 00:00:00"),
            145: pd.Timestamp("1961-02-01 00:00:00"),
            146: pd.Timestamp("1961-03-01 00:00:00"),
            147: pd.Timestamp("1961-04-01 00:00:00"),
            148: pd.Timestamp("1961-05-01 00:00:00"),
            149: pd.Timestamp("1961-06-01 00:00:00"),
            150: pd.Timestamp("1961-07-01 00:00:00"),
            151: pd.Timestamp("1961-08-01 00:00:00"),
            152: pd.Timestamp("1961-09-01 00:00:00"),
            153: pd.Timestamp("1961-10-01 00:00:00"),
            154: pd.Timestamp("1961-11-01 00:00:00"),
            155: pd.Timestamp("1961-12-01 00:00:00"),
            156: pd.Timestamp("1962-01-01 00:00:00"),
            157: pd.Timestamp("1962-02-01 00:00:00"),
            158: pd.Timestamp("1962-03-01 00:00:00"),
        },
        "fcst": {
            144: 447.12997053137497,
            145: 416.8943415790874,
            146: 461.31785253090857,
            147: 480.8468434929286,
            148: 499.02818626377126,
            149: 561.930444765741,
            150: 651.3312292286936,
            151: 647.5086856403731,
            152: 529.1660189014996,
            153: 474.192343615874,
            154: 405.4960170447968,
            155: 445.52827993367737,
            156: 461.5181965591764,
            157: 430.273735190953,
            158: 476.08344090751103,
        },
        "fcst_lower": {
            144: 433.2370906368733,
            145: 398.5247559820268,
            146: 439.3662867951251,
            147: 455.8208409855942,
            148: 471.26615527283343,
            149: 531.6788344286617,
            150: 618.7798929852893,
            151: 612.809707925232,
            152: 492.4447904672473,
            153: 435.5545618347569,
            154: 365.03235749440626,
            155: 403.3176496676579,
            156: 417.6300789110101,
            157: 384.7699283837861,
            158: 429.0193782143196,
        },
        "fcst_upper": {
            144: 461.02285042587664,
            145: 435.26392717614794,
            146: 483.269418266692,
            147: 505.872846000263,
            148: 526.7902172547091,
            149: 592.1820551028202,
            150: 683.8825654720979,
            151: 682.2076633555141,
            152: 565.8872473357519,
            153: 512.8301253969911,
            154: 445.9596765951874,
            155: 487.7389101996968,
            156: 505.40631420734263,
            157: 475.7775419981199,
            158: 523.1475036007025,
        },
    }
)

AIR_FCST_15_THETA_INCL_HIST_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("1949-01-01 00:00:00"),
            1: pd.Timestamp("1949-02-01 00:00:00"),
            2: pd.Timestamp("1949-03-01 00:00:00"),
            3: pd.Timestamp("1949-04-01 00:00:00"),
            4: pd.Timestamp("1949-05-01 00:00:00"),
            5: pd.Timestamp("1949-06-01 00:00:00"),
            6: pd.Timestamp("1949-07-01 00:00:00"),
            7: pd.Timestamp("1949-08-01 00:00:00"),
            8: pd.Timestamp("1949-09-01 00:00:00"),
            9: pd.Timestamp("1949-10-01 00:00:00"),
            10: pd.Timestamp("1949-11-01 00:00:00"),
            11: pd.Timestamp("1949-12-01 00:00:00"),
            12: pd.Timestamp("1950-01-01 00:00:00"),
            13: pd.Timestamp("1950-02-01 00:00:00"),
            14: pd.Timestamp("1950-03-01 00:00:00"),
            15: pd.Timestamp("1950-04-01 00:00:00"),
            16: pd.Timestamp("1950-05-01 00:00:00"),
            17: pd.Timestamp("1950-06-01 00:00:00"),
            18: pd.Timestamp("1950-07-01 00:00:00"),
            19: pd.Timestamp("1950-08-01 00:00:00"),
            20: pd.Timestamp("1950-09-01 00:00:00"),
            21: pd.Timestamp("1950-10-01 00:00:00"),
            22: pd.Timestamp("1950-11-01 00:00:00"),
            23: pd.Timestamp("1950-12-01 00:00:00"),
            24: pd.Timestamp("1951-01-01 00:00:00"),
            25: pd.Timestamp("1951-02-01 00:00:00"),
            26: pd.Timestamp("1951-03-01 00:00:00"),
            27: pd.Timestamp("1951-04-01 00:00:00"),
            28: pd.Timestamp("1951-05-01 00:00:00"),
            29: pd.Timestamp("1951-06-01 00:00:00"),
            30: pd.Timestamp("1951-07-01 00:00:00"),
            31: pd.Timestamp("1951-08-01 00:00:00"),
            32: pd.Timestamp("1951-09-01 00:00:00"),
            33: pd.Timestamp("1951-10-01 00:00:00"),
            34: pd.Timestamp("1951-11-01 00:00:00"),
            35: pd.Timestamp("1951-12-01 00:00:00"),
            36: pd.Timestamp("1952-01-01 00:00:00"),
            37: pd.Timestamp("1952-02-01 00:00:00"),
            38: pd.Timestamp("1952-03-01 00:00:00"),
            39: pd.Timestamp("1952-04-01 00:00:00"),
            40: pd.Timestamp("1952-05-01 00:00:00"),
            41: pd.Timestamp("1952-06-01 00:00:00"),
            42: pd.Timestamp("1952-07-01 00:00:00"),
            43: pd.Timestamp("1952-08-01 00:00:00"),
            44: pd.Timestamp("1952-09-01 00:00:00"),
            45: pd.Timestamp("1952-10-01 00:00:00"),
            46: pd.Timestamp("1952-11-01 00:00:00"),
            47: pd.Timestamp("1952-12-01 00:00:00"),
            48: pd.Timestamp("1953-01-01 00:00:00"),
            49: pd.Timestamp("1953-02-01 00:00:00"),
            50: pd.Timestamp("1953-03-01 00:00:00"),
            51: pd.Timestamp("1953-04-01 00:00:00"),
            52: pd.Timestamp("1953-05-01 00:00:00"),
            53: pd.Timestamp("1953-06-01 00:00:00"),
            54: pd.Timestamp("1953-07-01 00:00:00"),
            55: pd.Timestamp("1953-08-01 00:00:00"),
            56: pd.Timestamp("1953-09-01 00:00:00"),
            57: pd.Timestamp("1953-10-01 00:00:00"),
            58: pd.Timestamp("1953-11-01 00:00:00"),
            59: pd.Timestamp("1953-12-01 00:00:00"),
            60: pd.Timestamp("1954-01-01 00:00:00"),
            61: pd.Timestamp("1954-02-01 00:00:00"),
            62: pd.Timestamp("1954-03-01 00:00:00"),
            63: pd.Timestamp("1954-04-01 00:00:00"),
            64: pd.Timestamp("1954-05-01 00:00:00"),
            65: pd.Timestamp("1954-06-01 00:00:00"),
            66: pd.Timestamp("1954-07-01 00:00:00"),
            67: pd.Timestamp("1954-08-01 00:00:00"),
            68: pd.Timestamp("1954-09-01 00:00:00"),
            69: pd.Timestamp("1954-10-01 00:00:00"),
            70: pd.Timestamp("1954-11-01 00:00:00"),
            71: pd.Timestamp("1954-12-01 00:00:00"),
            72: pd.Timestamp("1955-01-01 00:00:00"),
            73: pd.Timestamp("1955-02-01 00:00:00"),
            74: pd.Timestamp("1955-03-01 00:00:00"),
            75: pd.Timestamp("1955-04-01 00:00:00"),
            76: pd.Timestamp("1955-05-01 00:00:00"),
            77: pd.Timestamp("1955-06-01 00:00:00"),
            78: pd.Timestamp("1955-07-01 00:00:00"),
            79: pd.Timestamp("1955-08-01 00:00:00"),
            80: pd.Timestamp("1955-09-01 00:00:00"),
            81: pd.Timestamp("1955-10-01 00:00:00"),
            82: pd.Timestamp("1955-11-01 00:00:00"),
            83: pd.Timestamp("1955-12-01 00:00:00"),
            84: pd.Timestamp("1956-01-01 00:00:00"),
            85: pd.Timestamp("1956-02-01 00:00:00"),
            86: pd.Timestamp("1956-03-01 00:00:00"),
            87: pd.Timestamp("1956-04-01 00:00:00"),
            88: pd.Timestamp("1956-05-01 00:00:00"),
            89: pd.Timestamp("1956-06-01 00:00:00"),
            90: pd.Timestamp("1956-07-01 00:00:00"),
            91: pd.Timestamp("1956-08-01 00:00:00"),
            92: pd.Timestamp("1956-09-01 00:00:00"),
            93: pd.Timestamp("1956-10-01 00:00:00"),
            94: pd.Timestamp("1956-11-01 00:00:00"),
            95: pd.Timestamp("1956-12-01 00:00:00"),
            96: pd.Timestamp("1957-01-01 00:00:00"),
            97: pd.Timestamp("1957-02-01 00:00:00"),
            98: pd.Timestamp("1957-03-01 00:00:00"),
            99: pd.Timestamp("1957-04-01 00:00:00"),
            100: pd.Timestamp("1957-05-01 00:00:00"),
            101: pd.Timestamp("1957-06-01 00:00:00"),
            102: pd.Timestamp("1957-07-01 00:00:00"),
            103: pd.Timestamp("1957-08-01 00:00:00"),
            104: pd.Timestamp("1957-09-01 00:00:00"),
            105: pd.Timestamp("1957-10-01 00:00:00"),
            106: pd.Timestamp("1957-11-01 00:00:00"),
            107: pd.Timestamp("1957-12-01 00:00:00"),
            108: pd.Timestamp("1958-01-01 00:00:00"),
            109: pd.Timestamp("1958-02-01 00:00:00"),
            110: pd.Timestamp("1958-03-01 00:00:00"),
            111: pd.Timestamp("1958-04-01 00:00:00"),
            112: pd.Timestamp("1958-05-01 00:00:00"),
            113: pd.Timestamp("1958-06-01 00:00:00"),
            114: pd.Timestamp("1958-07-01 00:00:00"),
            115: pd.Timestamp("1958-08-01 00:00:00"),
            116: pd.Timestamp("1958-09-01 00:00:00"),
            117: pd.Timestamp("1958-10-01 00:00:00"),
            118: pd.Timestamp("1958-11-01 00:00:00"),
            119: pd.Timestamp("1958-12-01 00:00:00"),
            120: pd.Timestamp("1959-01-01 00:00:00"),
            121: pd.Timestamp("1959-02-01 00:00:00"),
            122: pd.Timestamp("1959-03-01 00:00:00"),
            123: pd.Timestamp("1959-04-01 00:00:00"),
            124: pd.Timestamp("1959-05-01 00:00:00"),
            125: pd.Timestamp("1959-06-01 00:00:00"),
            126: pd.Timestamp("1959-07-01 00:00:00"),
            127: pd.Timestamp("1959-08-01 00:00:00"),
            128: pd.Timestamp("1959-09-01 00:00:00"),
            129: pd.Timestamp("1959-10-01 00:00:00"),
            130: pd.Timestamp("1959-11-01 00:00:00"),
            131: pd.Timestamp("1959-12-01 00:00:00"),
            132: pd.Timestamp("1960-01-01 00:00:00"),
            133: pd.Timestamp("1960-02-01 00:00:00"),
            134: pd.Timestamp("1960-03-01 00:00:00"),
            135: pd.Timestamp("1960-04-01 00:00:00"),
            136: pd.Timestamp("1960-05-01 00:00:00"),
            137: pd.Timestamp("1960-06-01 00:00:00"),
            138: pd.Timestamp("1960-07-01 00:00:00"),
            139: pd.Timestamp("1960-08-01 00:00:00"),
            140: pd.Timestamp("1960-09-01 00:00:00"),
            141: pd.Timestamp("1960-10-01 00:00:00"),
            142: pd.Timestamp("1960-11-01 00:00:00"),
            143: pd.Timestamp("1960-12-01 00:00:00"),
            144: pd.Timestamp("1961-01-01 00:00:00"),
            145: pd.Timestamp("1961-02-01 00:00:00"),
            146: pd.Timestamp("1961-03-01 00:00:00"),
            147: pd.Timestamp("1961-04-01 00:00:00"),
            148: pd.Timestamp("1961-05-01 00:00:00"),
            149: pd.Timestamp("1961-06-01 00:00:00"),
            150: pd.Timestamp("1961-07-01 00:00:00"),
            151: pd.Timestamp("1961-08-01 00:00:00"),
            152: pd.Timestamp("1961-09-01 00:00:00"),
            153: pd.Timestamp("1961-10-01 00:00:00"),
            154: pd.Timestamp("1961-11-01 00:00:00"),
            155: pd.Timestamp("1961-12-01 00:00:00"),
            156: pd.Timestamp("1962-01-01 00:00:00"),
            157: pd.Timestamp("1962-02-01 00:00:00"),
            158: pd.Timestamp("1962-03-01 00:00:00"),
        },
        "fcst": {
            0: 111.7905799360185,
            1: 119.82472512125055,
            2: 131.14459763569317,
            3: 125.56839165628531,
            4: 119.86272981994105,
            5: 135.83754410276745,
            6: 149.55111916170793,
            7: 146.12359906035255,
            8: 136.27856835553996,
            9: 115.64427567629895,
            10: 102.25215634207217,
            11: 120.38471744168451,
            12: 117.35820040626832,
            13: 121.44248345318918,
            14: 140.0599508848024,
            15: 133.21249298615248,
            16: 128.58840829955054,
            17: 140.0348065110792,
            18: 162.27805574794718,
            19: 168.02484366344544,
            20: 154.03555563776092,
            21: 135.37939004533348,
            22: 115.53586109927777,
            23: 131.21208878884235,
            24: 138.72758314130448,
            25: 149.21937140707112,
            26: 169.00075796540986,
            27: 166.57241737737675,
            28: 159.05859598099732,
            29: 188.879914591256,
            30: 195.8045812609248,
            31: 198.9715149173661,
            32: 177.7624336703185,
            33: 159.0700433371404,
            34: 140.43926674756182,
            35: 165.40813871220757,
            36: 167.42689952684722,
            37: 172.68120091210173,
            38: 204.59250345246096,
            39: 183.0075063052462,
            40: 180.27613965475433,
            41: 200.42189495419697,
            42: 233.85450605995783,
            43: 232.524089620245,
            44: 211.86339315758138,
            45: 184.14462724282973,
            46: 165.9542633395202,
            47: 192.54116557192023,
            48: 196.25783252624996,
            49: 192.36244942841208,
            50: 226.828657700216,
            51: 224.41477542438489,
            52: 233.22798750966024,
            53: 254.0565182678204,
            54: 266.8152196684842,
            55: 264.07814373253336,
            56: 236.1362421912865,
            57: 209.25459449535404,
            58: 183.47994401913246,
            59: 201.82240017025208,
            60: 204.28588594510396,
            61: 195.8401409673102,
            62: 221.12524361013655,
            63: 226.88852560331415,
            64: 225.9544728418766,
            65: 260.73687669847055,
            66: 291.84692199486915,
            67: 294.55118676724476,
            68: 255.90070689766952,
            69: 226.47456225183024,
            70: 197.69540185551435,
            71: 227.50811360264754,
            72: 233.05463830418378,
            73: 228.25729667155636,
            74: 271.47136641390483,
            75: 261.4129617151037,
            76: 268.40009231006866,
            77: 306.99864737061506,
            78: 349.93431398344404,
            79: 350.048832841657,
            80: 303.5865637959524,
            81: 269.1129924197851,
            82: 236.9594208289742,
            83: 266.7433297777855,
            84: 282.068689883387,
            85: 268.8725429163281,
            86: 317.8642039675062,
            87: 309.201394113091,
            88: 314.908132497328,
            89: 369.19523059903406,
            90: 415.1461165352526,
            91: 402.3677847544506,
            92: 349.4869939218194,
            93: 305.3769087729116,
            94: 265.6678894162828,
            95: 302.3260379839074,
            96: 311.712248384884,
            97: 297.9299486067005,
            98: 345.0876423584656,
            99: 343.9674701251021,
            100: 353.933299360484,
            101: 414.9914085002251,
            102: 468.0116723310664,
            103: 462.0048489728523,
            104: 393.4166570765834,
            105: 347.9647719545135,
            106: 301.83137622262024,
            107: 336.5507955174962,
            108: 345.3549206140498,
            109: 320.8234223900151,
            110: 362.3406494213253,
            111: 357.5627964594115,
            112: 358.2424853667907,
            113: 418.23268478045287,
            114: 486.7977555338237,
            115: 488.06843993619253,
            116: 418.7248814627883,
            117: 354.3490302950448,
            118: 310.2744981728548,
            119: 341.32915090561175,
            120: 347.3038516002216,
            121: 335.1924314711943,
            122: 382.54589002115716,
            123: 407.6269097110198,
            124: 409.7820711563323,
            125: 477.1963934572989,
            126: 538.7376784778799,
            127: 543.7565580990243,
            128: 458.4631486624468,
            129: 408.34402250877673,
            130: 349.9485743891647,
            131: 395.49678803805085,
            132: 416.5802995582341,
            133: 387.70931695887504,
            134: 431.0200916138051,
            135: 437.26216103376447,
            136: 473.84558017158747,
            137: 530.3687089879487,
            138: 617.7571252407732,
            139: 616.1542255966216,
            140: 495.06058306948773,
            141: 452.4724502903674,
            142: 392.20374950190387,
            143: 427.71221850789254,
            144: 447.12997053137497,
            145: 416.8943415790874,
            146: 461.31785253090857,
            147: 480.8468434929286,
            148: 499.02818626377126,
            149: 561.930444765741,
            150: 651.3312292286936,
            151: 647.5086856403731,
            152: 529.1660189014996,
            153: 474.192343615874,
            154: 405.4960170447968,
            155: 445.52827993367737,
            156: 461.5181965591764,
            157: 430.273735190953,
            158: 476.08344090751103,
        },
        "fcst_lower": {
            0: 97.8977000415168,
            1: 105.93184522674885,
            2: 117.25171774119147,
            3: 111.67551176178361,
            4: 105.96984992543935,
            5: 121.94466420826575,
            6: 135.65823926720623,
            7: 132.23071916585084,
            8: 122.38568846103826,
            9: 101.75139578179724,
            10: 88.35927644757047,
            11: 106.49183754718281,
            12: 103.46532051176662,
            13: 107.54960355868748,
            14: 126.16707099030069,
            15: 119.31961309165078,
            16: 114.69552840504883,
            17: 126.1419266165775,
            18: 148.38517585344547,
            19: 154.13196376894373,
            20: 140.14267574325922,
            21: 121.48651015083178,
            22: 101.64298120477606,
            23: 117.31920889434065,
            24: 124.83470324680277,
            25: 135.32649151256942,
            26: 155.10787807090816,
            27: 152.67953748287505,
            28: 145.16571608649562,
            29: 174.9870346967543,
            30: 181.9117013664231,
            31: 185.0786350228644,
            32: 163.8695537758168,
            33: 145.1771634426387,
            34: 126.54638685306011,
            35: 151.51525881770587,
            36: 153.53401963234552,
            37: 158.78832101760003,
            38: 190.69962355795926,
            39: 169.1146264107445,
            40: 166.38325976025263,
            41: 186.52901505969527,
            42: 219.96162616545612,
            43: 218.6312097257433,
            44: 197.97051326307968,
            45: 170.25174734832802,
            46: 152.0613834450185,
            47: 178.64828567741853,
            48: 182.36495263174825,
            49: 178.46956953391037,
            50: 212.9357778057143,
            51: 210.52189552988318,
            52: 219.33510761515853,
            53: 240.1636383733187,
            54: 252.9223397739825,
            55: 250.18526383803166,
            56: 222.2433622967848,
            57: 195.36171460085234,
            58: 169.58706412463076,
            59: 187.92952027575038,
            60: 190.39300605060225,
            61: 181.9472610728085,
            62: 207.23236371563485,
            63: 212.99564570881245,
            64: 212.0615929473749,
            65: 246.84399680396885,
            66: 277.9540421003675,
            67: 280.6583068727431,
            68: 242.00782700316782,
            69: 212.58168235732853,
            70: 183.80252196101264,
            71: 213.61523370814584,
            72: 219.16175840968208,
            73: 214.36441677705466,
            74: 257.57848651940316,
            75: 247.52008182060197,
            76: 254.50721241556695,
            77: 293.1057674761134,
            78: 336.04143408894237,
            79: 336.1559529471553,
            80: 289.6936839014507,
            81: 255.22011252528338,
            82: 223.0665409344725,
            83: 252.8504498832838,
            84: 268.1758099888853,
            85: 254.97966302182638,
            86: 303.97132407300455,
            87: 295.3085142185893,
            88: 301.0152526028263,
            89: 355.3023507045324,
            90: 401.2532366407509,
            91: 388.47490485994894,
            92: 335.59411402731774,
            93: 291.4840288784099,
            94: 251.77500952178107,
            95: 288.4331580894057,
            96: 297.8193684903823,
            97: 284.03706871219885,
            98: 331.1947624639639,
            99: 330.07459023060045,
            100: 340.0404194659823,
            101: 401.0985286057234,
            102: 454.1187924365647,
            103: 448.1119690783506,
            104: 379.52377718208174,
            105: 334.0718920600118,
            106: 287.93849632811856,
            107: 322.65791562299455,
            108: 331.4620407195481,
            109: 306.9305424955134,
            110: 348.4477695268236,
            111: 343.66991656490984,
            112: 344.349605472289,
            113: 404.3398048859512,
            114: 472.90487563932203,
            115: 474.17556004169086,
            116: 404.8320015682866,
            117: 340.45615040054315,
            118: 296.38161827835313,
            119: 327.4362710111101,
            120: 333.41097170571993,
            121: 321.29955157669264,
            122: 368.6530101266555,
            123: 393.7340298165181,
            124: 395.8891912618306,
            125: 463.3035135627972,
            126: 524.8447985833782,
            127: 529.8636782045227,
            128: 444.57026876794515,
            129: 394.45114261427506,
            130: 336.05569449466304,
            131: 381.6039081435492,
            132: 402.68741966373244,
            133: 373.81643706437336,
            134: 417.12721171930343,
            135: 423.3692811392628,
            136: 459.9527002770858,
            137: 516.475829093447,
            138: 603.8642453462716,
            139: 602.2613457021199,
            140: 481.16770317498606,
            141: 438.57957039586574,
            142: 378.3108696074022,
            143: 413.81933861339087,
            144: 433.2370906368733,
            145: 398.5247559820268,
            146: 439.3662867951251,
            147: 455.8208409855942,
            148: 471.26615527283343,
            149: 531.6788344286617,
            150: 618.7798929852893,
            151: 612.809707925232,
            152: 492.4447904672473,
            153: 435.5545618347569,
            154: 365.03235749440626,
            155: 403.3176496676579,
            156: 417.6300789110101,
            157: 384.7699283837861,
            158: 429.0193782143196,
        },
        "fcst_upper": {
            0: 125.6834598305202,
            1: 133.71760501575224,
            2: 145.03747753019488,
            3: 139.46127155078702,
            4: 133.75560971444276,
            5: 149.73042399726916,
            6: 163.44399905620963,
            7: 160.01647895485425,
            8: 150.17144825004166,
            9: 129.53715557080065,
            10: 116.14503623657387,
            11: 134.2775973361862,
            12: 131.25108030077,
            13: 135.33536334769087,
            14: 153.9528307793041,
            15: 147.1053728806542,
            16: 142.48128819405224,
            17: 153.9276864055809,
            18: 176.17093564244888,
            19: 181.91772355794714,
            20: 167.92843553226263,
            21: 149.27226993983518,
            22: 129.42874099377946,
            23: 145.10496868334405,
            24: 152.62046303580618,
            25: 163.11225130157283,
            26: 182.89363785991156,
            27: 180.46529727187846,
            28: 172.95147587549903,
            29: 202.77279448575771,
            30: 209.6974611554265,
            31: 212.8643948118678,
            32: 191.6553135648202,
            33: 172.9629232316421,
            34: 154.33214664206352,
            35: 179.30101860670928,
            36: 181.31977942134893,
            37: 186.57408080660343,
            38: 218.48538334696266,
            39: 196.9003861997479,
            40: 194.16901954925603,
            41: 214.31477484869868,
            42: 247.74738595445953,
            43: 246.4169695147467,
            44: 225.7562730520831,
            45: 198.03750713733143,
            46: 179.8471432340219,
            47: 206.43404546642194,
            48: 210.15071242075166,
            49: 206.25532932291378,
            50: 240.72153759471772,
            51: 238.3076553188866,
            52: 247.12086740416194,
            53: 267.9493981623221,
            54: 280.70809956298586,
            55: 277.97102362703504,
            56: 250.0291220857882,
            57: 223.14747438985574,
            58: 197.37282391363416,
            59: 215.71528006475378,
            60: 218.17876583960566,
            61: 209.7330208618119,
            62: 235.01812350463825,
            63: 240.78140549781585,
            64: 239.8473527363783,
            65: 274.6297565929722,
            66: 305.7398018893708,
            67: 308.44406666174643,
            68: 269.7935867921712,
            69: 240.36744214633194,
            70: 211.58828175001605,
            71: 241.40099349714924,
            72: 246.9475181986855,
            73: 242.15017656605806,
            74: 285.3642463084065,
            75: 275.30584160960535,
            76: 282.29297220457033,
            77: 320.89152726511674,
            78: 363.8271938779457,
            79: 363.94171273615865,
            80: 317.47944369045405,
            81: 283.00587231428676,
            82: 250.8523007234759,
            83: 280.6362096722872,
            84: 295.96156977788866,
            85: 282.76542281082976,
            86: 331.7570838620079,
            87: 323.0942740075927,
            88: 328.80101239182966,
            89: 383.08811049353574,
            90: 429.03899642975426,
            91: 416.2606646489523,
            92: 363.3798738163211,
            93: 319.26978866741325,
            94: 279.56076931078445,
            95: 316.21891787840906,
            96: 325.60512827938567,
            97: 311.8228285012022,
            98: 358.98052225296726,
            99: 357.8603500196038,
            100: 367.8261792549857,
            101: 428.88428839472675,
            102: 481.90455222556807,
            103: 475.897728867354,
            104: 407.3095369710851,
            105: 361.85765184901516,
            106: 315.7242561171219,
            107: 350.4436754119979,
            108: 359.24780050855145,
            109: 334.71630228451676,
            110: 376.23352931582696,
            111: 371.4556763539132,
            112: 372.13536526129235,
            113: 432.12556467495455,
            114: 500.6906354283254,
            115: 501.9613198306942,
            116: 432.61776135728996,
            117: 368.2419101895465,
            118: 324.1673780673565,
            119: 355.22203080011343,
            120: 361.1967314947233,
            121: 349.085311365696,
            122: 396.43876991565884,
            123: 421.51978960552145,
            124: 423.67495105083395,
            125: 491.08927335180056,
            126: 552.6305583723815,
            127: 557.649437993526,
            128: 472.3560285569485,
            129: 422.2369024032784,
            130: 363.8414542836664,
            131: 409.3896679325525,
            132: 430.4731794527358,
            133: 401.6021968533767,
            134: 444.9129715083068,
            135: 451.15504092826615,
            136: 487.73846006608915,
            137: 544.2615888824504,
            138: 631.6500051352749,
            139: 630.0471054911233,
            140: 508.9534629639894,
            141: 466.3653301848691,
            142: 406.09662939640555,
            143: 441.6050984023942,
            144: 461.02285042587664,
            145: 435.26392717614794,
            146: 483.269418266692,
            147: 505.872846000263,
            148: 526.7902172547091,
            149: 592.1820551028202,
            150: 683.8825654720979,
            151: 682.2076633555141,
            152: 565.8872473357519,
            153: 512.8301253969911,
            154: 445.9596765951874,
            155: 487.7389101996968,
            156: 505.40631420734263,
            157: 475.7775419981199,
            158: 523.1475036007025,
        },
    }
)

PEYTON_FCST_30_THETA_SM_11 = pd.DataFrame(
    {
        "time": {
            364: pd.Timestamp("2013-05-01 00:00:00"),
            365: pd.Timestamp("2013-05-02 00:00:00"),
            366: pd.Timestamp("2013-05-03 00:00:00"),
            367: pd.Timestamp("2013-05-04 00:00:00"),
            368: pd.Timestamp("2013-05-05 00:00:00"),
            369: pd.Timestamp("2013-05-06 00:00:00"),
            370: pd.Timestamp("2013-05-07 00:00:00"),
            371: pd.Timestamp("2013-05-08 00:00:00"),
            372: pd.Timestamp("2013-05-09 00:00:00"),
            373: pd.Timestamp("2013-05-10 00:00:00"),
            374: pd.Timestamp("2013-05-11 00:00:00"),
            375: pd.Timestamp("2013-05-12 00:00:00"),
            376: pd.Timestamp("2013-05-13 00:00:00"),
            377: pd.Timestamp("2013-05-14 00:00:00"),
            378: pd.Timestamp("2013-05-15 00:00:00"),
            379: pd.Timestamp("2013-05-16 00:00:00"),
            380: pd.Timestamp("2013-05-17 00:00:00"),
            381: pd.Timestamp("2013-05-18 00:00:00"),
            382: pd.Timestamp("2013-05-19 00:00:00"),
            383: pd.Timestamp("2013-05-20 00:00:00"),
            384: pd.Timestamp("2013-05-21 00:00:00"),
            385: pd.Timestamp("2013-05-22 00:00:00"),
            386: pd.Timestamp("2013-05-23 00:00:00"),
            387: pd.Timestamp("2013-05-24 00:00:00"),
            388: pd.Timestamp("2013-05-25 00:00:00"),
            389: pd.Timestamp("2013-05-26 00:00:00"),
            390: pd.Timestamp("2013-05-27 00:00:00"),
            391: pd.Timestamp("2013-05-28 00:00:00"),
            392: pd.Timestamp("2013-05-29 00:00:00"),
            393: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            364: 7.990746152944167,
            365: 7.990926126046517,
            366: 7.991106099148868,
            367: 7.991286072251218,
            368: 7.991466045353568,
            369: 7.991646018455919,
            370: 7.991825991558269,
            371: 7.992005964660619,
            372: 7.99218593776297,
            373: 7.99236591086532,
            374: 7.9925458839676695,
            375: 7.99272585707002,
            376: 7.99290583017237,
            377: 7.99308580327472,
            378: 7.99326577637707,
            379: 7.993445749479421,
            380: 7.993625722581771,
            381: 7.993805695684121,
            382: 7.993985668786472,
            383: 7.994165641888822,
            384: 7.994345614991172,
            385: 7.994525588093523,
            386: 7.994705561195873,
            387: 7.994885534298223,
            388: 7.995065507400573,
            389: 7.995245480502923,
            390: 7.995425453605273,
            391: 7.995605426707624,
            392: 7.995785399809974,
            393: 7.995965372912324,
        },
        "fcst_lower": {
            364: 7.0419752895941565,
            365: 7.0170579491646325,
            366: 6.992771335282274,
            367: 6.9690701579034275,
            368: 6.9459143008917215,
            369: 6.923268030500096,
            370: 6.9010993530507765,
            371: 6.879379488815659,
            372: 6.858082437392524,
            373: 6.837184615849763,
            374: 6.816664555280485,
            375: 6.796502644639974,
            376: 6.776680913161631,
            377: 6.757182844479369,
            378: 6.737993216985914,
            379: 6.719097966038125,
            380: 6.7004840644626515,
            381: 6.682139418476238,
            382: 6.664052776657809,
            383: 6.646213650025969,
            384: 6.628612241609547,
            385: 6.611239384168365,
            386: 6.5940864849403065,
            387: 6.577145476469456,
            388: 6.560408772716754,
            389: 6.543869229775718,
            390: 6.527520110616059,
            391: 6.511355053361671,
            392: 6.495368042679324,
            393: 6.479553383913212,
        },
        "fcst_upper": {
            364: 8.939517016294179,
            365: 8.964794302928402,
            366: 8.989440863015462,
            367: 9.013501986599008,
            368: 9.037017789815415,
            369: 9.06002400641174,
            370: 9.082552630065761,
            371: 9.10463244050558,
            372: 9.126289438133416,
            373: 9.147547205880876,
            374: 9.168427212654855,
            375: 9.188949069500067,
            376: 9.20913074718311,
            377: 9.22898876207007,
            378: 9.248538335768227,
            379: 9.267793532920718,
            380: 9.28676738070089,
            381: 9.305471972892004,
            382: 9.323918560915134,
            383: 9.342117633751673,
            384: 9.360078988372797,
            385: 9.377811792018681,
            386: 9.395324637451438,
            387: 9.41262559212699,
            388: 9.429722242084392,
            389: 9.44662173123013,
            390: 9.463330796594487,
            391: 9.479855800053578,
            392: 9.496202756940624,
            393: 9.512377361911437,
        },
    }
)

PEYTON_FCST_30_THETA_INCL_HIST_SM_11 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2012-05-02 00:00:00"),
            1: pd.Timestamp("2012-05-03 00:00:00"),
            2: pd.Timestamp("2012-05-04 00:00:00"),
            3: pd.Timestamp("2012-05-05 00:00:00"),
            4: pd.Timestamp("2012-05-06 00:00:00"),
            5: pd.Timestamp("2012-05-07 00:00:00"),
            6: pd.Timestamp("2012-05-08 00:00:00"),
            7: pd.Timestamp("2012-05-09 00:00:00"),
            8: pd.Timestamp("2012-05-10 00:00:00"),
            9: pd.Timestamp("2012-05-11 00:00:00"),
            10: pd.Timestamp("2012-05-12 00:00:00"),
            11: pd.Timestamp("2012-05-13 00:00:00"),
            12: pd.Timestamp("2012-05-14 00:00:00"),
            13: pd.Timestamp("2012-05-15 00:00:00"),
            14: pd.Timestamp("2012-05-16 00:00:00"),
            15: pd.Timestamp("2012-05-17 00:00:00"),
            16: pd.Timestamp("2012-05-18 00:00:00"),
            17: pd.Timestamp("2012-05-19 00:00:00"),
            18: pd.Timestamp("2012-05-20 00:00:00"),
            19: pd.Timestamp("2012-05-21 00:00:00"),
            20: pd.Timestamp("2012-05-22 00:00:00"),
            21: pd.Timestamp("2012-05-23 00:00:00"),
            22: pd.Timestamp("2012-05-24 00:00:00"),
            23: pd.Timestamp("2012-05-25 00:00:00"),
            24: pd.Timestamp("2012-05-26 00:00:00"),
            25: pd.Timestamp("2012-05-27 00:00:00"),
            26: pd.Timestamp("2012-05-28 00:00:00"),
            27: pd.Timestamp("2012-05-29 00:00:00"),
            28: pd.Timestamp("2012-05-30 00:00:00"),
            29: pd.Timestamp("2012-05-31 00:00:00"),
            30: pd.Timestamp("2012-06-01 00:00:00"),
            31: pd.Timestamp("2012-06-02 00:00:00"),
            32: pd.Timestamp("2012-06-03 00:00:00"),
            33: pd.Timestamp("2012-06-04 00:00:00"),
            34: pd.Timestamp("2012-06-05 00:00:00"),
            35: pd.Timestamp("2012-06-06 00:00:00"),
            36: pd.Timestamp("2012-06-07 00:00:00"),
            37: pd.Timestamp("2012-06-08 00:00:00"),
            38: pd.Timestamp("2012-06-09 00:00:00"),
            39: pd.Timestamp("2012-06-10 00:00:00"),
            40: pd.Timestamp("2012-06-11 00:00:00"),
            41: pd.Timestamp("2012-06-12 00:00:00"),
            42: pd.Timestamp("2012-06-13 00:00:00"),
            43: pd.Timestamp("2012-06-14 00:00:00"),
            44: pd.Timestamp("2012-06-15 00:00:00"),
            45: pd.Timestamp("2012-06-16 00:00:00"),
            46: pd.Timestamp("2012-06-17 00:00:00"),
            47: pd.Timestamp("2012-06-18 00:00:00"),
            48: pd.Timestamp("2012-06-19 00:00:00"),
            49: pd.Timestamp("2012-06-20 00:00:00"),
            50: pd.Timestamp("2012-06-21 00:00:00"),
            51: pd.Timestamp("2012-06-22 00:00:00"),
            52: pd.Timestamp("2012-06-23 00:00:00"),
            53: pd.Timestamp("2012-06-24 00:00:00"),
            54: pd.Timestamp("2012-06-25 00:00:00"),
            55: pd.Timestamp("2012-06-26 00:00:00"),
            56: pd.Timestamp("2012-06-27 00:00:00"),
            57: pd.Timestamp("2012-06-28 00:00:00"),
            58: pd.Timestamp("2012-06-29 00:00:00"),
            59: pd.Timestamp("2012-06-30 00:00:00"),
            60: pd.Timestamp("2012-07-01 00:00:00"),
            61: pd.Timestamp("2012-07-02 00:00:00"),
            62: pd.Timestamp("2012-07-03 00:00:00"),
            63: pd.Timestamp("2012-07-04 00:00:00"),
            64: pd.Timestamp("2012-07-05 00:00:00"),
            65: pd.Timestamp("2012-07-06 00:00:00"),
            66: pd.Timestamp("2012-07-07 00:00:00"),
            67: pd.Timestamp("2012-07-08 00:00:00"),
            68: pd.Timestamp("2012-07-09 00:00:00"),
            69: pd.Timestamp("2012-07-10 00:00:00"),
            70: pd.Timestamp("2012-07-11 00:00:00"),
            71: pd.Timestamp("2012-07-12 00:00:00"),
            72: pd.Timestamp("2012-07-13 00:00:00"),
            73: pd.Timestamp("2012-07-14 00:00:00"),
            74: pd.Timestamp("2012-07-15 00:00:00"),
            75: pd.Timestamp("2012-07-16 00:00:00"),
            76: pd.Timestamp("2012-07-17 00:00:00"),
            77: pd.Timestamp("2012-07-18 00:00:00"),
            78: pd.Timestamp("2012-07-19 00:00:00"),
            79: pd.Timestamp("2012-07-20 00:00:00"),
            80: pd.Timestamp("2012-07-21 00:00:00"),
            81: pd.Timestamp("2012-07-22 00:00:00"),
            82: pd.Timestamp("2012-07-23 00:00:00"),
            83: pd.Timestamp("2012-07-24 00:00:00"),
            84: pd.Timestamp("2012-07-25 00:00:00"),
            85: pd.Timestamp("2012-07-26 00:00:00"),
            86: pd.Timestamp("2012-07-27 00:00:00"),
            87: pd.Timestamp("2012-07-28 00:00:00"),
            88: pd.Timestamp("2012-07-29 00:00:00"),
            89: pd.Timestamp("2012-07-30 00:00:00"),
            90: pd.Timestamp("2012-07-31 00:00:00"),
            91: pd.Timestamp("2012-08-01 00:00:00"),
            92: pd.Timestamp("2012-08-02 00:00:00"),
            93: pd.Timestamp("2012-08-03 00:00:00"),
            94: pd.Timestamp("2012-08-04 00:00:00"),
            95: pd.Timestamp("2012-08-05 00:00:00"),
            96: pd.Timestamp("2012-08-06 00:00:00"),
            97: pd.Timestamp("2012-08-07 00:00:00"),
            98: pd.Timestamp("2012-08-08 00:00:00"),
            99: pd.Timestamp("2012-08-09 00:00:00"),
            100: pd.Timestamp("2012-08-10 00:00:00"),
            101: pd.Timestamp("2012-08-11 00:00:00"),
            102: pd.Timestamp("2012-08-12 00:00:00"),
            103: pd.Timestamp("2012-08-13 00:00:00"),
            104: pd.Timestamp("2012-08-14 00:00:00"),
            105: pd.Timestamp("2012-08-15 00:00:00"),
            106: pd.Timestamp("2012-08-16 00:00:00"),
            107: pd.Timestamp("2012-08-17 00:00:00"),
            108: pd.Timestamp("2012-08-18 00:00:00"),
            109: pd.Timestamp("2012-08-19 00:00:00"),
            110: pd.Timestamp("2012-08-20 00:00:00"),
            111: pd.Timestamp("2012-08-21 00:00:00"),
            112: pd.Timestamp("2012-08-22 00:00:00"),
            113: pd.Timestamp("2012-08-23 00:00:00"),
            114: pd.Timestamp("2012-08-24 00:00:00"),
            115: pd.Timestamp("2012-08-25 00:00:00"),
            116: pd.Timestamp("2012-08-26 00:00:00"),
            117: pd.Timestamp("2012-08-27 00:00:00"),
            118: pd.Timestamp("2012-08-28 00:00:00"),
            119: pd.Timestamp("2012-08-29 00:00:00"),
            120: pd.Timestamp("2012-08-30 00:00:00"),
            121: pd.Timestamp("2012-08-31 00:00:00"),
            122: pd.Timestamp("2012-09-01 00:00:00"),
            123: pd.Timestamp("2012-09-02 00:00:00"),
            124: pd.Timestamp("2012-09-03 00:00:00"),
            125: pd.Timestamp("2012-09-04 00:00:00"),
            126: pd.Timestamp("2012-09-05 00:00:00"),
            127: pd.Timestamp("2012-09-06 00:00:00"),
            128: pd.Timestamp("2012-09-07 00:00:00"),
            129: pd.Timestamp("2012-09-08 00:00:00"),
            130: pd.Timestamp("2012-09-09 00:00:00"),
            131: pd.Timestamp("2012-09-10 00:00:00"),
            132: pd.Timestamp("2012-09-11 00:00:00"),
            133: pd.Timestamp("2012-09-12 00:00:00"),
            134: pd.Timestamp("2012-09-13 00:00:00"),
            135: pd.Timestamp("2012-09-14 00:00:00"),
            136: pd.Timestamp("2012-09-15 00:00:00"),
            137: pd.Timestamp("2012-09-16 00:00:00"),
            138: pd.Timestamp("2012-09-17 00:00:00"),
            139: pd.Timestamp("2012-09-18 00:00:00"),
            140: pd.Timestamp("2012-09-19 00:00:00"),
            141: pd.Timestamp("2012-09-20 00:00:00"),
            142: pd.Timestamp("2012-09-21 00:00:00"),
            143: pd.Timestamp("2012-09-22 00:00:00"),
            144: pd.Timestamp("2012-09-23 00:00:00"),
            145: pd.Timestamp("2012-09-24 00:00:00"),
            146: pd.Timestamp("2012-09-25 00:00:00"),
            147: pd.Timestamp("2012-09-26 00:00:00"),
            148: pd.Timestamp("2012-09-27 00:00:00"),
            149: pd.Timestamp("2012-09-28 00:00:00"),
            150: pd.Timestamp("2012-09-29 00:00:00"),
            151: pd.Timestamp("2012-09-30 00:00:00"),
            152: pd.Timestamp("2012-10-01 00:00:00"),
            153: pd.Timestamp("2012-10-02 00:00:00"),
            154: pd.Timestamp("2012-10-03 00:00:00"),
            155: pd.Timestamp("2012-10-04 00:00:00"),
            156: pd.Timestamp("2012-10-05 00:00:00"),
            157: pd.Timestamp("2012-10-06 00:00:00"),
            158: pd.Timestamp("2012-10-07 00:00:00"),
            159: pd.Timestamp("2012-10-08 00:00:00"),
            160: pd.Timestamp("2012-10-09 00:00:00"),
            161: pd.Timestamp("2012-10-10 00:00:00"),
            162: pd.Timestamp("2012-10-11 00:00:00"),
            163: pd.Timestamp("2012-10-12 00:00:00"),
            164: pd.Timestamp("2012-10-13 00:00:00"),
            165: pd.Timestamp("2012-10-14 00:00:00"),
            166: pd.Timestamp("2012-10-15 00:00:00"),
            167: pd.Timestamp("2012-10-16 00:00:00"),
            168: pd.Timestamp("2012-10-17 00:00:00"),
            169: pd.Timestamp("2012-10-18 00:00:00"),
            170: pd.Timestamp("2012-10-19 00:00:00"),
            171: pd.Timestamp("2012-10-20 00:00:00"),
            172: pd.Timestamp("2012-10-21 00:00:00"),
            173: pd.Timestamp("2012-10-22 00:00:00"),
            174: pd.Timestamp("2012-10-23 00:00:00"),
            175: pd.Timestamp("2012-10-24 00:00:00"),
            176: pd.Timestamp("2012-10-25 00:00:00"),
            177: pd.Timestamp("2012-10-26 00:00:00"),
            178: pd.Timestamp("2012-10-27 00:00:00"),
            179: pd.Timestamp("2012-10-28 00:00:00"),
            180: pd.Timestamp("2012-10-29 00:00:00"),
            181: pd.Timestamp("2012-10-30 00:00:00"),
            182: pd.Timestamp("2012-10-31 00:00:00"),
            183: pd.Timestamp("2012-11-01 00:00:00"),
            184: pd.Timestamp("2012-11-02 00:00:00"),
            185: pd.Timestamp("2012-11-03 00:00:00"),
            186: pd.Timestamp("2012-11-04 00:00:00"),
            187: pd.Timestamp("2012-11-05 00:00:00"),
            188: pd.Timestamp("2012-11-06 00:00:00"),
            189: pd.Timestamp("2012-11-07 00:00:00"),
            190: pd.Timestamp("2012-11-08 00:00:00"),
            191: pd.Timestamp("2012-11-09 00:00:00"),
            192: pd.Timestamp("2012-11-10 00:00:00"),
            193: pd.Timestamp("2012-11-11 00:00:00"),
            194: pd.Timestamp("2012-11-12 00:00:00"),
            195: pd.Timestamp("2012-11-13 00:00:00"),
            196: pd.Timestamp("2012-11-14 00:00:00"),
            197: pd.Timestamp("2012-11-15 00:00:00"),
            198: pd.Timestamp("2012-11-16 00:00:00"),
            199: pd.Timestamp("2012-11-17 00:00:00"),
            200: pd.Timestamp("2012-11-18 00:00:00"),
            201: pd.Timestamp("2012-11-19 00:00:00"),
            202: pd.Timestamp("2012-11-20 00:00:00"),
            203: pd.Timestamp("2012-11-21 00:00:00"),
            204: pd.Timestamp("2012-11-22 00:00:00"),
            205: pd.Timestamp("2012-11-23 00:00:00"),
            206: pd.Timestamp("2012-11-24 00:00:00"),
            207: pd.Timestamp("2012-11-25 00:00:00"),
            208: pd.Timestamp("2012-11-26 00:00:00"),
            209: pd.Timestamp("2012-11-27 00:00:00"),
            210: pd.Timestamp("2012-11-28 00:00:00"),
            211: pd.Timestamp("2012-11-29 00:00:00"),
            212: pd.Timestamp("2012-11-30 00:00:00"),
            213: pd.Timestamp("2012-12-01 00:00:00"),
            214: pd.Timestamp("2012-12-02 00:00:00"),
            215: pd.Timestamp("2012-12-03 00:00:00"),
            216: pd.Timestamp("2012-12-04 00:00:00"),
            217: pd.Timestamp("2012-12-05 00:00:00"),
            218: pd.Timestamp("2012-12-06 00:00:00"),
            219: pd.Timestamp("2012-12-07 00:00:00"),
            220: pd.Timestamp("2012-12-08 00:00:00"),
            221: pd.Timestamp("2012-12-09 00:00:00"),
            222: pd.Timestamp("2012-12-10 00:00:00"),
            223: pd.Timestamp("2012-12-11 00:00:00"),
            224: pd.Timestamp("2012-12-12 00:00:00"),
            225: pd.Timestamp("2012-12-13 00:00:00"),
            226: pd.Timestamp("2012-12-14 00:00:00"),
            227: pd.Timestamp("2012-12-15 00:00:00"),
            228: pd.Timestamp("2012-12-16 00:00:00"),
            229: pd.Timestamp("2012-12-17 00:00:00"),
            230: pd.Timestamp("2012-12-18 00:00:00"),
            231: pd.Timestamp("2012-12-19 00:00:00"),
            232: pd.Timestamp("2012-12-20 00:00:00"),
            233: pd.Timestamp("2012-12-21 00:00:00"),
            234: pd.Timestamp("2012-12-22 00:00:00"),
            235: pd.Timestamp("2012-12-23 00:00:00"),
            236: pd.Timestamp("2012-12-24 00:00:00"),
            237: pd.Timestamp("2012-12-25 00:00:00"),
            238: pd.Timestamp("2012-12-26 00:00:00"),
            239: pd.Timestamp("2012-12-27 00:00:00"),
            240: pd.Timestamp("2012-12-28 00:00:00"),
            241: pd.Timestamp("2012-12-29 00:00:00"),
            242: pd.Timestamp("2012-12-30 00:00:00"),
            243: pd.Timestamp("2012-12-31 00:00:00"),
            244: pd.Timestamp("2013-01-01 00:00:00"),
            245: pd.Timestamp("2013-01-02 00:00:00"),
            246: pd.Timestamp("2013-01-03 00:00:00"),
            247: pd.Timestamp("2013-01-04 00:00:00"),
            248: pd.Timestamp("2013-01-05 00:00:00"),
            249: pd.Timestamp("2013-01-06 00:00:00"),
            250: pd.Timestamp("2013-01-07 00:00:00"),
            251: pd.Timestamp("2013-01-08 00:00:00"),
            252: pd.Timestamp("2013-01-09 00:00:00"),
            253: pd.Timestamp("2013-01-10 00:00:00"),
            254: pd.Timestamp("2013-01-11 00:00:00"),
            255: pd.Timestamp("2013-01-12 00:00:00"),
            256: pd.Timestamp("2013-01-13 00:00:00"),
            257: pd.Timestamp("2013-01-14 00:00:00"),
            258: pd.Timestamp("2013-01-15 00:00:00"),
            259: pd.Timestamp("2013-01-16 00:00:00"),
            260: pd.Timestamp("2013-01-17 00:00:00"),
            261: pd.Timestamp("2013-01-18 00:00:00"),
            262: pd.Timestamp("2013-01-19 00:00:00"),
            263: pd.Timestamp("2013-01-20 00:00:00"),
            264: pd.Timestamp("2013-01-21 00:00:00"),
            265: pd.Timestamp("2013-01-22 00:00:00"),
            266: pd.Timestamp("2013-01-23 00:00:00"),
            267: pd.Timestamp("2013-01-24 00:00:00"),
            268: pd.Timestamp("2013-01-25 00:00:00"),
            269: pd.Timestamp("2013-01-26 00:00:00"),
            270: pd.Timestamp("2013-01-27 00:00:00"),
            271: pd.Timestamp("2013-01-28 00:00:00"),
            272: pd.Timestamp("2013-01-29 00:00:00"),
            273: pd.Timestamp("2013-01-30 00:00:00"),
            274: pd.Timestamp("2013-01-31 00:00:00"),
            275: pd.Timestamp("2013-02-01 00:00:00"),
            276: pd.Timestamp("2013-02-02 00:00:00"),
            277: pd.Timestamp("2013-02-03 00:00:00"),
            278: pd.Timestamp("2013-02-04 00:00:00"),
            279: pd.Timestamp("2013-02-05 00:00:00"),
            280: pd.Timestamp("2013-02-06 00:00:00"),
            281: pd.Timestamp("2013-02-07 00:00:00"),
            282: pd.Timestamp("2013-02-08 00:00:00"),
            283: pd.Timestamp("2013-02-09 00:00:00"),
            284: pd.Timestamp("2013-02-10 00:00:00"),
            285: pd.Timestamp("2013-02-11 00:00:00"),
            286: pd.Timestamp("2013-02-12 00:00:00"),
            287: pd.Timestamp("2013-02-13 00:00:00"),
            288: pd.Timestamp("2013-02-14 00:00:00"),
            289: pd.Timestamp("2013-02-15 00:00:00"),
            290: pd.Timestamp("2013-02-16 00:00:00"),
            291: pd.Timestamp("2013-02-17 00:00:00"),
            292: pd.Timestamp("2013-02-18 00:00:00"),
            293: pd.Timestamp("2013-02-19 00:00:00"),
            294: pd.Timestamp("2013-02-20 00:00:00"),
            295: pd.Timestamp("2013-02-21 00:00:00"),
            296: pd.Timestamp("2013-02-22 00:00:00"),
            297: pd.Timestamp("2013-02-23 00:00:00"),
            298: pd.Timestamp("2013-02-24 00:00:00"),
            299: pd.Timestamp("2013-02-25 00:00:00"),
            300: pd.Timestamp("2013-02-26 00:00:00"),
            301: pd.Timestamp("2013-02-27 00:00:00"),
            302: pd.Timestamp("2013-02-28 00:00:00"),
            303: pd.Timestamp("2013-03-01 00:00:00"),
            304: pd.Timestamp("2013-03-02 00:00:00"),
            305: pd.Timestamp("2013-03-03 00:00:00"),
            306: pd.Timestamp("2013-03-04 00:00:00"),
            307: pd.Timestamp("2013-03-05 00:00:00"),
            308: pd.Timestamp("2013-03-06 00:00:00"),
            309: pd.Timestamp("2013-03-07 00:00:00"),
            310: pd.Timestamp("2013-03-08 00:00:00"),
            311: pd.Timestamp("2013-03-09 00:00:00"),
            312: pd.Timestamp("2013-03-10 00:00:00"),
            313: pd.Timestamp("2013-03-11 00:00:00"),
            314: pd.Timestamp("2013-03-12 00:00:00"),
            315: pd.Timestamp("2013-03-13 00:00:00"),
            316: pd.Timestamp("2013-03-14 00:00:00"),
            317: pd.Timestamp("2013-03-15 00:00:00"),
            318: pd.Timestamp("2013-03-16 00:00:00"),
            319: pd.Timestamp("2013-03-17 00:00:00"),
            320: pd.Timestamp("2013-03-18 00:00:00"),
            321: pd.Timestamp("2013-03-19 00:00:00"),
            322: pd.Timestamp("2013-03-20 00:00:00"),
            323: pd.Timestamp("2013-03-21 00:00:00"),
            324: pd.Timestamp("2013-03-22 00:00:00"),
            325: pd.Timestamp("2013-03-23 00:00:00"),
            326: pd.Timestamp("2013-03-24 00:00:00"),
            327: pd.Timestamp("2013-03-25 00:00:00"),
            328: pd.Timestamp("2013-03-26 00:00:00"),
            329: pd.Timestamp("2013-03-27 00:00:00"),
            330: pd.Timestamp("2013-03-28 00:00:00"),
            331: pd.Timestamp("2013-03-29 00:00:00"),
            332: pd.Timestamp("2013-03-30 00:00:00"),
            333: pd.Timestamp("2013-03-31 00:00:00"),
            334: pd.Timestamp("2013-04-01 00:00:00"),
            335: pd.Timestamp("2013-04-02 00:00:00"),
            336: pd.Timestamp("2013-04-03 00:00:00"),
            337: pd.Timestamp("2013-04-04 00:00:00"),
            338: pd.Timestamp("2013-04-05 00:00:00"),
            339: pd.Timestamp("2013-04-06 00:00:00"),
            340: pd.Timestamp("2013-04-07 00:00:00"),
            341: pd.Timestamp("2013-04-08 00:00:00"),
            342: pd.Timestamp("2013-04-09 00:00:00"),
            343: pd.Timestamp("2013-04-10 00:00:00"),
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            345: pd.Timestamp("2013-04-12 00:00:00"),
            346: pd.Timestamp("2013-04-13 00:00:00"),
            347: pd.Timestamp("2013-04-14 00:00:00"),
            348: pd.Timestamp("2013-04-15 00:00:00"),
            349: pd.Timestamp("2013-04-16 00:00:00"),
            350: pd.Timestamp("2013-04-17 00:00:00"),
            351: pd.Timestamp("2013-04-18 00:00:00"),
            352: pd.Timestamp("2013-04-19 00:00:00"),
            353: pd.Timestamp("2013-04-20 00:00:00"),
            354: pd.Timestamp("2013-04-21 00:00:00"),
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            391: 6.511355053361671,
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            393: 6.479553383913212,
        },
        "fcst_upper": {
            0: 9.191342280907197,
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            392: 9.496202756940624,
            393: 9.512377361911437,
        },
    }
)

AIR_FCST_15_THETA_SM_12 = pd.DataFrame(
    {
        "time": {
            t1: t2
            for t1, t2 in zip(
                range(144, 159), pd.date_range("1961-01-01", freq="MS", periods=15)
            )
        },
        "fcst": {
            144: 448.2087900770604,
            145: 418.01704627454444,
            146: 462.6226697900576,
            147: 482.01829558715565,
            148: 500.1199199052061,
            149: 563.0404558048393,
            150: 652.4307042492908,
            151: 648.3239024316182,
            152: 529.5672929614743,
            153: 474.35684250110404,
            154: 405.53660708977384,
            155: 445.4969133060624,
            156: 462.6108836763946,
            157: 431.41313113608305,
            158: 477.40873250978916,
        },
        "fcst_lower": {
            144: 434.30437309420444,
            145: 399.62726053256915,
            146: 440.6445982723293,
            147: 456.9606214005715,
            148: 472.3217465183875,
            149: 532.7487102023447,
            150: 619.8355932715863,
            151: 613.577785128364,
            152: 492.79578122134967,
            153: 435.6658166209759,
            154: 365.0168971739888,
            155: 403.227559163114,
            156: 418.66148427589525,
            157: 385.8455865932043,
            158: 430.2785667592864,
        },
        "fcst_upper": {
            144: 462.1132070599164,
            145: 436.40683201651973,
            146: 484.60074130778594,
            147: 507.0759697737398,
            148: 527.9180932920247,
            149: 593.332201407334,
            150: 685.0258152269953,
            151: 683.0700197348724,
            152: 566.338804701599,
            153: 513.0478683812322,
            154: 446.05631700555887,
            155: 487.76626744901085,
            156: 506.560283076894,
            157: 476.9806756789618,
            158: 524.538898260292,
        },
    }
)

AIR_FCST_15_THETA_INCL_HIST_SM_12 = pd.DataFrame(
    {
        "time": {
            t1: t2
            for t1, t2 in zip(
                range(0, 159), pd.date_range("1949-01-01", freq="MS", periods=160)
            )
        },
        "fcst": {
            0: 111.79670896456088,
            1: 119.78825978861222,
            2: 131.108639837203,
            3: 125.52125593777158,
            4: 119.79843574271331,
            5: 135.75952312142212,
            6: 149.49159600425662,
            7: 146.09031891691237,
            8: 136.25293824456878,
            9: 115.61942178015076,
            10: 102.25193807456077,
            11: 120.44840075601132,
            12: 117.57986049609734,
            13: 121.44514623826416,
            14: 140.03514994329447,
            15: 133.1701517951673,
            16: 128.53096202289535,
            17: 139.96545336056764,
            18: 162.2264130013029,
            19: 167.995131473987,
            20: 154.01883207274443,
            21: 135.37786929814985,
            22: 115.55460771339975,
            23: 131.27520720016872,
            24: 138.89032716777527,
            25: 149.22675401621964,
            26: 168.97633126489228,
            27: 166.53323486843237,
            28: 159.00128987511988,
            29: 188.81812462715843,
            30: 195.73756366126267,
            31: 198.92834163791827,
            32: 177.74204274489412,
            33: 159.08148136111856,
            34: 140.4765248149028,
            35: 165.47945476038055,
            36: 167.5886863324693,
            37: 172.695207108439,
            38: 204.56967949380495,
            39: 182.96453987357236,
            40: 180.2358420528077,
            41: 200.3807802955463,
            42: 233.8099668939168,
            43: 232.4679406889497,
            44: 211.83271294828242,
            45: 184.14245575784503,
            46: 165.98485018929784,
            47: 192.59435476206696,
            48: 196.30459247582291,
            49: 192.3859344363729,
            50: 226.8244653984648,
            51: 224.3931842749689,
            52: 233.21798211135595,
            53: 254.05492079939162,
            54: 266.8038301871632,
            55: 264.0560956511354,
            56: 236.11447734133853,
            57: 209.23401387498993,
            58: 183.4710183561026,
            59: 201.82992019907178,
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        },
        "fcst_lower": {
            0: 97.89229198170486,
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            25: 135.32233703336362,
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            48: 182.4001754929669,
            49: 178.4815174535169,
            50: 212.9200484156088,
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            52: 219.31356512849993,
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            60: 190.40649456379688,
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            63: 212.99449235124655,
            64: 212.05405794570137,
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            142: 378.18368555989997,
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            144: 434.30437309420444,
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            155: 403.227559163114,
            156: 418.66148427589525,
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            158: 430.2785667592864,
        },
        "fcst_upper": {
            0: 125.7011259474169,
            1: 133.69267677146823,
            2: 145.013056820059,
            3: 139.4256729206276,
            4: 133.70285272556933,
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            6: 163.39601298711264,
            7: 159.9947358997684,
            8: 150.1573552274248,
            9: 129.52383876300678,
            10: 116.15635505741679,
            11: 134.35281773886734,
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            13: 135.34956322112018,
            14: 153.9395669261505,
            15: 147.07456877802332,
            16: 142.43537900575137,
            17: 153.86987034342366,
            18: 176.1308299841589,
            19: 181.899548456843,
            20: 167.92324905560045,
            21: 149.28228628100587,
            22: 129.45902469625577,
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            24: 152.7947441506313,
            25: 163.13117099907566,
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            27: 180.4376518512884,
            28: 172.9057068579759,
            29: 202.72254161001445,
            30: 209.6419806441187,
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            32: 191.64645972775014,
            33: 172.98589834397458,
            34: 154.38094179775882,
            35: 179.38387174323657,
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            37: 186.59962409129503,
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            39: 196.86895685642838,
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            41: 214.2851972784023,
            42: 247.7143838767728,
            43: 246.37235767180573,
            44: 225.73712993113844,
            45: 198.04687274070105,
            46: 179.88926717215386,
            47: 206.49877174492298,
            48: 210.20900945867893,
            49: 206.29035141922893,
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            52: 247.12239909421197,
            53: 267.95933778224764,
            54: 280.7082471700192,
            55: 277.9605126339914,
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            60: 218.21532852950892,
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            62: 235.04571689461167,
            63: 240.8033263169586,
            64: 239.8628919114134,
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            120: 361.4763812924191,
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            150: 685.0258152269953,
            151: 683.0700197348724,
            152: 566.338804701599,
            153: 513.0478683812322,
            154: 446.05631700555887,
            155: 487.76626744901085,
            156: 506.560283076894,
            157: 476.9806756789618,
            158: 524.538898260292,
        },
    }
)

PEYTON_FCST_30_THETA_SM_12 = pd.DataFrame(
    {
        "time": {
            364: pd.Timestamp("2013-05-01 00:00:00"),
            365: pd.Timestamp("2013-05-02 00:00:00"),
            366: pd.Timestamp("2013-05-03 00:00:00"),
            367: pd.Timestamp("2013-05-04 00:00:00"),
            368: pd.Timestamp("2013-05-05 00:00:00"),
            369: pd.Timestamp("2013-05-06 00:00:00"),
            370: pd.Timestamp("2013-05-07 00:00:00"),
            371: pd.Timestamp("2013-05-08 00:00:00"),
            372: pd.Timestamp("2013-05-09 00:00:00"),
            373: pd.Timestamp("2013-05-10 00:00:00"),
            374: pd.Timestamp("2013-05-11 00:00:00"),
            375: pd.Timestamp("2013-05-12 00:00:00"),
            376: pd.Timestamp("2013-05-13 00:00:00"),
            377: pd.Timestamp("2013-05-14 00:00:00"),
            378: pd.Timestamp("2013-05-15 00:00:00"),
            379: pd.Timestamp("2013-05-16 00:00:00"),
            380: pd.Timestamp("2013-05-17 00:00:00"),
            381: pd.Timestamp("2013-05-18 00:00:00"),
            382: pd.Timestamp("2013-05-19 00:00:00"),
            383: pd.Timestamp("2013-05-20 00:00:00"),
            384: pd.Timestamp("2013-05-21 00:00:00"),
            385: pd.Timestamp("2013-05-22 00:00:00"),
            386: pd.Timestamp("2013-05-23 00:00:00"),
            387: pd.Timestamp("2013-05-24 00:00:00"),
            388: pd.Timestamp("2013-05-25 00:00:00"),
            389: pd.Timestamp("2013-05-26 00:00:00"),
            390: pd.Timestamp("2013-05-27 00:00:00"),
            391: pd.Timestamp("2013-05-28 00:00:00"),
            392: pd.Timestamp("2013-05-29 00:00:00"),
            393: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            364: 7.990738547693687,
            365: 7.990918520796038,
            366: 7.991098493898388,
            367: 7.991278467000738,
            368: 7.991458440103089,
            369: 7.991638413205439,
            370: 7.991818386307789,
            371: 7.99199835941014,
            372: 7.9921783325124895,
            373: 7.9923583056148395,
            374: 7.9925382787171895,
            375: 7.99271825181954,
            376: 7.99289822492189,
            377: 7.99307819802424,
            378: 7.993258171126591,
            379: 7.993438144228941,
            380: 7.993618117331291,
            381: 7.993798090433641,
            382: 7.993978063535992,
            383: 7.994158036638342,
            384: 7.994338009740692,
            385: 7.994517982843043,
            386: 7.994697955945393,
            387: 7.9948779290477425,
            388: 7.9950579021500925,
            389: 7.995237875252443,
            390: 7.995417848354793,
            391: 7.995597821457143,
            392: 7.995777794559494,
            393: 7.995957767661844,
        },
        "fcst_lower": {
            364: 7.041967684275796,
            365: 7.017052582945252,
            366: 6.992768098417386,
            367: 6.969068952186619,
            368: 6.9459150379376045,
            369: 6.923270630348319,
            370: 6.90110374302005,
            371: 6.879385602554656,
            372: 6.858090214087461,
            373: 6.8371939995578686,
            374: 6.816675494365484,
            375: 6.796515091290942,
            376: 6.7766948229804775,
            377: 6.757198176125218,
            378: 6.738009931866966,
            379: 6.719116028043378,
            380: 6.700503439727285,
            381: 6.682160075175554,
            382: 6.664074684825556,
            383: 6.646236781393549,
            384: 6.628636569463221,
            385: 6.611264883222001,
            386: 6.594113131221635,
            387: 6.577173247218081,
            388: 6.560437646292465,
            389: 6.5438991855758175,
            390: 6.527551129000639,
            391: 6.511387115585909,
            392: 6.495401130831999,
            393: 6.479587480860724,
        },
        "fcst_upper": {
            364: 8.939509411111578,
            365: 8.964784458646825,
            366: 8.98942888937939,
            367: 9.013487981814857,
            368: 9.037001842268573,
            369: 9.060006196062558,
            370: 9.082533029595528,
            371: 9.104611116265623,
            372: 9.126266450937518,
            373: 9.14752261167181,
            374: 9.168401063068895,
            375: 9.188921412348138,
            376: 9.209101626863303,
            377: 9.228958219923262,
            378: 9.248506410386216,
            379: 9.267760260414503,
            380: 9.286732794935297,
            381: 9.305436105691728,
            382: 9.323881442246428,
            383: 9.342079291883135,
            384: 9.360039450018162,
            385: 9.377771082464085,
            386: 9.39528278066915,
            387: 9.412582610877404,
            388: 9.42967815800772,
            389: 9.44657656492907,
            390: 9.463284567708946,
            391: 9.479808527328377,
            392: 9.49615445828699,
            393: 9.512328054462964,
        },
    }
)

PEYTON_FCST_30_THETA_INCL_HIST_SM_12 = pd.DataFrame(
    {
        "time": {
            0: pd.Timestamp("2012-05-02 00:00:00"),
            1: pd.Timestamp("2012-05-03 00:00:00"),
            2: pd.Timestamp("2012-05-04 00:00:00"),
            3: pd.Timestamp("2012-05-05 00:00:00"),
            4: pd.Timestamp("2012-05-06 00:00:00"),
            5: pd.Timestamp("2012-05-07 00:00:00"),
            6: pd.Timestamp("2012-05-08 00:00:00"),
            7: pd.Timestamp("2012-05-09 00:00:00"),
            8: pd.Timestamp("2012-05-10 00:00:00"),
            9: pd.Timestamp("2012-05-11 00:00:00"),
            10: pd.Timestamp("2012-05-12 00:00:00"),
            11: pd.Timestamp("2012-05-13 00:00:00"),
            12: pd.Timestamp("2012-05-14 00:00:00"),
            13: pd.Timestamp("2012-05-15 00:00:00"),
            14: pd.Timestamp("2012-05-16 00:00:00"),
            15: pd.Timestamp("2012-05-17 00:00:00"),
            16: pd.Timestamp("2012-05-18 00:00:00"),
            17: pd.Timestamp("2012-05-19 00:00:00"),
            18: pd.Timestamp("2012-05-20 00:00:00"),
            19: pd.Timestamp("2012-05-21 00:00:00"),
            20: pd.Timestamp("2012-05-22 00:00:00"),
            21: pd.Timestamp("2012-05-23 00:00:00"),
            22: pd.Timestamp("2012-05-24 00:00:00"),
            23: pd.Timestamp("2012-05-25 00:00:00"),
            24: pd.Timestamp("2012-05-26 00:00:00"),
            25: pd.Timestamp("2012-05-27 00:00:00"),
            26: pd.Timestamp("2012-05-28 00:00:00"),
            27: pd.Timestamp("2012-05-29 00:00:00"),
            28: pd.Timestamp("2012-05-30 00:00:00"),
            29: pd.Timestamp("2012-05-31 00:00:00"),
            30: pd.Timestamp("2012-06-01 00:00:00"),
            31: pd.Timestamp("2012-06-02 00:00:00"),
            32: pd.Timestamp("2012-06-03 00:00:00"),
            33: pd.Timestamp("2012-06-04 00:00:00"),
            34: pd.Timestamp("2012-06-05 00:00:00"),
            35: pd.Timestamp("2012-06-06 00:00:00"),
            36: pd.Timestamp("2012-06-07 00:00:00"),
            37: pd.Timestamp("2012-06-08 00:00:00"),
            38: pd.Timestamp("2012-06-09 00:00:00"),
            39: pd.Timestamp("2012-06-10 00:00:00"),
            40: pd.Timestamp("2012-06-11 00:00:00"),
            41: pd.Timestamp("2012-06-12 00:00:00"),
            42: pd.Timestamp("2012-06-13 00:00:00"),
            43: pd.Timestamp("2012-06-14 00:00:00"),
            44: pd.Timestamp("2012-06-15 00:00:00"),
            45: pd.Timestamp("2012-06-16 00:00:00"),
            46: pd.Timestamp("2012-06-17 00:00:00"),
            47: pd.Timestamp("2012-06-18 00:00:00"),
            48: pd.Timestamp("2012-06-19 00:00:00"),
            49: pd.Timestamp("2012-06-20 00:00:00"),
            50: pd.Timestamp("2012-06-21 00:00:00"),
            51: pd.Timestamp("2012-06-22 00:00:00"),
            52: pd.Timestamp("2012-06-23 00:00:00"),
            53: pd.Timestamp("2012-06-24 00:00:00"),
            54: pd.Timestamp("2012-06-25 00:00:00"),
            55: pd.Timestamp("2012-06-26 00:00:00"),
            56: pd.Timestamp("2012-06-27 00:00:00"),
            57: pd.Timestamp("2012-06-28 00:00:00"),
            58: pd.Timestamp("2012-06-29 00:00:00"),
            59: pd.Timestamp("2012-06-30 00:00:00"),
            60: pd.Timestamp("2012-07-01 00:00:00"),
            61: pd.Timestamp("2012-07-02 00:00:00"),
            62: pd.Timestamp("2012-07-03 00:00:00"),
            63: pd.Timestamp("2012-07-04 00:00:00"),
            64: pd.Timestamp("2012-07-05 00:00:00"),
            65: pd.Timestamp("2012-07-06 00:00:00"),
            66: pd.Timestamp("2012-07-07 00:00:00"),
            67: pd.Timestamp("2012-07-08 00:00:00"),
            68: pd.Timestamp("2012-07-09 00:00:00"),
            69: pd.Timestamp("2012-07-10 00:00:00"),
            70: pd.Timestamp("2012-07-11 00:00:00"),
            71: pd.Timestamp("2012-07-12 00:00:00"),
            72: pd.Timestamp("2012-07-13 00:00:00"),
            73: pd.Timestamp("2012-07-14 00:00:00"),
            74: pd.Timestamp("2012-07-15 00:00:00"),
            75: pd.Timestamp("2012-07-16 00:00:00"),
            76: pd.Timestamp("2012-07-17 00:00:00"),
            77: pd.Timestamp("2012-07-18 00:00:00"),
            78: pd.Timestamp("2012-07-19 00:00:00"),
            79: pd.Timestamp("2012-07-20 00:00:00"),
            80: pd.Timestamp("2012-07-21 00:00:00"),
            81: pd.Timestamp("2012-07-22 00:00:00"),
            82: pd.Timestamp("2012-07-23 00:00:00"),
            83: pd.Timestamp("2012-07-24 00:00:00"),
            84: pd.Timestamp("2012-07-25 00:00:00"),
            85: pd.Timestamp("2012-07-26 00:00:00"),
            86: pd.Timestamp("2012-07-27 00:00:00"),
            87: pd.Timestamp("2012-07-28 00:00:00"),
            88: pd.Timestamp("2012-07-29 00:00:00"),
            89: pd.Timestamp("2012-07-30 00:00:00"),
            90: pd.Timestamp("2012-07-31 00:00:00"),
            91: pd.Timestamp("2012-08-01 00:00:00"),
            92: pd.Timestamp("2012-08-02 00:00:00"),
            93: pd.Timestamp("2012-08-03 00:00:00"),
            94: pd.Timestamp("2012-08-04 00:00:00"),
            95: pd.Timestamp("2012-08-05 00:00:00"),
            96: pd.Timestamp("2012-08-06 00:00:00"),
            97: pd.Timestamp("2012-08-07 00:00:00"),
            98: pd.Timestamp("2012-08-08 00:00:00"),
            99: pd.Timestamp("2012-08-09 00:00:00"),
            100: pd.Timestamp("2012-08-10 00:00:00"),
            101: pd.Timestamp("2012-08-11 00:00:00"),
            102: pd.Timestamp("2012-08-12 00:00:00"),
            103: pd.Timestamp("2012-08-13 00:00:00"),
            104: pd.Timestamp("2012-08-14 00:00:00"),
            105: pd.Timestamp("2012-08-15 00:00:00"),
            106: pd.Timestamp("2012-08-16 00:00:00"),
            107: pd.Timestamp("2012-08-17 00:00:00"),
            108: pd.Timestamp("2012-08-18 00:00:00"),
            109: pd.Timestamp("2012-08-19 00:00:00"),
            110: pd.Timestamp("2012-08-20 00:00:00"),
            111: pd.Timestamp("2012-08-21 00:00:00"),
            112: pd.Timestamp("2012-08-22 00:00:00"),
            113: pd.Timestamp("2012-08-23 00:00:00"),
            114: pd.Timestamp("2012-08-24 00:00:00"),
            115: pd.Timestamp("2012-08-25 00:00:00"),
            116: pd.Timestamp("2012-08-26 00:00:00"),
            117: pd.Timestamp("2012-08-27 00:00:00"),
            118: pd.Timestamp("2012-08-28 00:00:00"),
            119: pd.Timestamp("2012-08-29 00:00:00"),
            120: pd.Timestamp("2012-08-30 00:00:00"),
            121: pd.Timestamp("2012-08-31 00:00:00"),
            122: pd.Timestamp("2012-09-01 00:00:00"),
            123: pd.Timestamp("2012-09-02 00:00:00"),
            124: pd.Timestamp("2012-09-03 00:00:00"),
            125: pd.Timestamp("2012-09-04 00:00:00"),
            126: pd.Timestamp("2012-09-05 00:00:00"),
            127: pd.Timestamp("2012-09-06 00:00:00"),
            128: pd.Timestamp("2012-09-07 00:00:00"),
            129: pd.Timestamp("2012-09-08 00:00:00"),
            130: pd.Timestamp("2012-09-09 00:00:00"),
            131: pd.Timestamp("2012-09-10 00:00:00"),
            132: pd.Timestamp("2012-09-11 00:00:00"),
            133: pd.Timestamp("2012-09-12 00:00:00"),
            134: pd.Timestamp("2012-09-13 00:00:00"),
            135: pd.Timestamp("2012-09-14 00:00:00"),
            136: pd.Timestamp("2012-09-15 00:00:00"),
            137: pd.Timestamp("2012-09-16 00:00:00"),
            138: pd.Timestamp("2012-09-17 00:00:00"),
            139: pd.Timestamp("2012-09-18 00:00:00"),
            140: pd.Timestamp("2012-09-19 00:00:00"),
            141: pd.Timestamp("2012-09-20 00:00:00"),
            142: pd.Timestamp("2012-09-21 00:00:00"),
            143: pd.Timestamp("2012-09-22 00:00:00"),
            144: pd.Timestamp("2012-09-23 00:00:00"),
            145: pd.Timestamp("2012-09-24 00:00:00"),
            146: pd.Timestamp("2012-09-25 00:00:00"),
            147: pd.Timestamp("2012-09-26 00:00:00"),
            148: pd.Timestamp("2012-09-27 00:00:00"),
            149: pd.Timestamp("2012-09-28 00:00:00"),
            150: pd.Timestamp("2012-09-29 00:00:00"),
            151: pd.Timestamp("2012-09-30 00:00:00"),
            152: pd.Timestamp("2012-10-01 00:00:00"),
            153: pd.Timestamp("2012-10-02 00:00:00"),
            154: pd.Timestamp("2012-10-03 00:00:00"),
            155: pd.Timestamp("2012-10-04 00:00:00"),
            156: pd.Timestamp("2012-10-05 00:00:00"),
            157: pd.Timestamp("2012-10-06 00:00:00"),
            158: pd.Timestamp("2012-10-07 00:00:00"),
            159: pd.Timestamp("2012-10-08 00:00:00"),
            160: pd.Timestamp("2012-10-09 00:00:00"),
            161: pd.Timestamp("2012-10-10 00:00:00"),
            162: pd.Timestamp("2012-10-11 00:00:00"),
            163: pd.Timestamp("2012-10-12 00:00:00"),
            164: pd.Timestamp("2012-10-13 00:00:00"),
            165: pd.Timestamp("2012-10-14 00:00:00"),
            166: pd.Timestamp("2012-10-15 00:00:00"),
            167: pd.Timestamp("2012-10-16 00:00:00"),
            168: pd.Timestamp("2012-10-17 00:00:00"),
            169: pd.Timestamp("2012-10-18 00:00:00"),
            170: pd.Timestamp("2012-10-19 00:00:00"),
            171: pd.Timestamp("2012-10-20 00:00:00"),
            172: pd.Timestamp("2012-10-21 00:00:00"),
            173: pd.Timestamp("2012-10-22 00:00:00"),
            174: pd.Timestamp("2012-10-23 00:00:00"),
            175: pd.Timestamp("2012-10-24 00:00:00"),
            176: pd.Timestamp("2012-10-25 00:00:00"),
            177: pd.Timestamp("2012-10-26 00:00:00"),
            178: pd.Timestamp("2012-10-27 00:00:00"),
            179: pd.Timestamp("2012-10-28 00:00:00"),
            180: pd.Timestamp("2012-10-29 00:00:00"),
            181: pd.Timestamp("2012-10-30 00:00:00"),
            182: pd.Timestamp("2012-10-31 00:00:00"),
            183: pd.Timestamp("2012-11-01 00:00:00"),
            184: pd.Timestamp("2012-11-02 00:00:00"),
            185: pd.Timestamp("2012-11-03 00:00:00"),
            186: pd.Timestamp("2012-11-04 00:00:00"),
            187: pd.Timestamp("2012-11-05 00:00:00"),
            188: pd.Timestamp("2012-11-06 00:00:00"),
            189: pd.Timestamp("2012-11-07 00:00:00"),
            190: pd.Timestamp("2012-11-08 00:00:00"),
            191: pd.Timestamp("2012-11-09 00:00:00"),
            192: pd.Timestamp("2012-11-10 00:00:00"),
            193: pd.Timestamp("2012-11-11 00:00:00"),
            194: pd.Timestamp("2012-11-12 00:00:00"),
            195: pd.Timestamp("2012-11-13 00:00:00"),
            196: pd.Timestamp("2012-11-14 00:00:00"),
            197: pd.Timestamp("2012-11-15 00:00:00"),
            198: pd.Timestamp("2012-11-16 00:00:00"),
            199: pd.Timestamp("2012-11-17 00:00:00"),
            200: pd.Timestamp("2012-11-18 00:00:00"),
            201: pd.Timestamp("2012-11-19 00:00:00"),
            202: pd.Timestamp("2012-11-20 00:00:00"),
            203: pd.Timestamp("2012-11-21 00:00:00"),
            204: pd.Timestamp("2012-11-22 00:00:00"),
            205: pd.Timestamp("2012-11-23 00:00:00"),
            206: pd.Timestamp("2012-11-24 00:00:00"),
            207: pd.Timestamp("2012-11-25 00:00:00"),
            208: pd.Timestamp("2012-11-26 00:00:00"),
            209: pd.Timestamp("2012-11-27 00:00:00"),
            210: pd.Timestamp("2012-11-28 00:00:00"),
            211: pd.Timestamp("2012-11-29 00:00:00"),
            212: pd.Timestamp("2012-11-30 00:00:00"),
            213: pd.Timestamp("2012-12-01 00:00:00"),
            214: pd.Timestamp("2012-12-02 00:00:00"),
            215: pd.Timestamp("2012-12-03 00:00:00"),
            216: pd.Timestamp("2012-12-04 00:00:00"),
            217: pd.Timestamp("2012-12-05 00:00:00"),
            218: pd.Timestamp("2012-12-06 00:00:00"),
            219: pd.Timestamp("2012-12-07 00:00:00"),
            220: pd.Timestamp("2012-12-08 00:00:00"),
            221: pd.Timestamp("2012-12-09 00:00:00"),
            222: pd.Timestamp("2012-12-10 00:00:00"),
            223: pd.Timestamp("2012-12-11 00:00:00"),
            224: pd.Timestamp("2012-12-12 00:00:00"),
            225: pd.Timestamp("2012-12-13 00:00:00"),
            226: pd.Timestamp("2012-12-14 00:00:00"),
            227: pd.Timestamp("2012-12-15 00:00:00"),
            228: pd.Timestamp("2012-12-16 00:00:00"),
            229: pd.Timestamp("2012-12-17 00:00:00"),
            230: pd.Timestamp("2012-12-18 00:00:00"),
            231: pd.Timestamp("2012-12-19 00:00:00"),
            232: pd.Timestamp("2012-12-20 00:00:00"),
            233: pd.Timestamp("2012-12-21 00:00:00"),
            234: pd.Timestamp("2012-12-22 00:00:00"),
            235: pd.Timestamp("2012-12-23 00:00:00"),
            236: pd.Timestamp("2012-12-24 00:00:00"),
            237: pd.Timestamp("2012-12-25 00:00:00"),
            238: pd.Timestamp("2012-12-26 00:00:00"),
            239: pd.Timestamp("2012-12-27 00:00:00"),
            240: pd.Timestamp("2012-12-28 00:00:00"),
            241: pd.Timestamp("2012-12-29 00:00:00"),
            242: pd.Timestamp("2012-12-30 00:00:00"),
            243: pd.Timestamp("2012-12-31 00:00:00"),
            244: pd.Timestamp("2013-01-01 00:00:00"),
            245: pd.Timestamp("2013-01-02 00:00:00"),
            246: pd.Timestamp("2013-01-03 00:00:00"),
            247: pd.Timestamp("2013-01-04 00:00:00"),
            248: pd.Timestamp("2013-01-05 00:00:00"),
            249: pd.Timestamp("2013-01-06 00:00:00"),
            250: pd.Timestamp("2013-01-07 00:00:00"),
            251: pd.Timestamp("2013-01-08 00:00:00"),
            252: pd.Timestamp("2013-01-09 00:00:00"),
            253: pd.Timestamp("2013-01-10 00:00:00"),
            254: pd.Timestamp("2013-01-11 00:00:00"),
            255: pd.Timestamp("2013-01-12 00:00:00"),
            256: pd.Timestamp("2013-01-13 00:00:00"),
            257: pd.Timestamp("2013-01-14 00:00:00"),
            258: pd.Timestamp("2013-01-15 00:00:00"),
            259: pd.Timestamp("2013-01-16 00:00:00"),
            260: pd.Timestamp("2013-01-17 00:00:00"),
            261: pd.Timestamp("2013-01-18 00:00:00"),
            262: pd.Timestamp("2013-01-19 00:00:00"),
            263: pd.Timestamp("2013-01-20 00:00:00"),
            264: pd.Timestamp("2013-01-21 00:00:00"),
            265: pd.Timestamp("2013-01-22 00:00:00"),
            266: pd.Timestamp("2013-01-23 00:00:00"),
            267: pd.Timestamp("2013-01-24 00:00:00"),
            268: pd.Timestamp("2013-01-25 00:00:00"),
            269: pd.Timestamp("2013-01-26 00:00:00"),
            270: pd.Timestamp("2013-01-27 00:00:00"),
            271: pd.Timestamp("2013-01-28 00:00:00"),
            272: pd.Timestamp("2013-01-29 00:00:00"),
            273: pd.Timestamp("2013-01-30 00:00:00"),
            274: pd.Timestamp("2013-01-31 00:00:00"),
            275: pd.Timestamp("2013-02-01 00:00:00"),
            276: pd.Timestamp("2013-02-02 00:00:00"),
            277: pd.Timestamp("2013-02-03 00:00:00"),
            278: pd.Timestamp("2013-02-04 00:00:00"),
            279: pd.Timestamp("2013-02-05 00:00:00"),
            280: pd.Timestamp("2013-02-06 00:00:00"),
            281: pd.Timestamp("2013-02-07 00:00:00"),
            282: pd.Timestamp("2013-02-08 00:00:00"),
            283: pd.Timestamp("2013-02-09 00:00:00"),
            284: pd.Timestamp("2013-02-10 00:00:00"),
            285: pd.Timestamp("2013-02-11 00:00:00"),
            286: pd.Timestamp("2013-02-12 00:00:00"),
            287: pd.Timestamp("2013-02-13 00:00:00"),
            288: pd.Timestamp("2013-02-14 00:00:00"),
            289: pd.Timestamp("2013-02-15 00:00:00"),
            290: pd.Timestamp("2013-02-16 00:00:00"),
            291: pd.Timestamp("2013-02-17 00:00:00"),
            292: pd.Timestamp("2013-02-18 00:00:00"),
            293: pd.Timestamp("2013-02-19 00:00:00"),
            294: pd.Timestamp("2013-02-20 00:00:00"),
            295: pd.Timestamp("2013-02-21 00:00:00"),
            296: pd.Timestamp("2013-02-22 00:00:00"),
            297: pd.Timestamp("2013-02-23 00:00:00"),
            298: pd.Timestamp("2013-02-24 00:00:00"),
            299: pd.Timestamp("2013-02-25 00:00:00"),
            300: pd.Timestamp("2013-02-26 00:00:00"),
            301: pd.Timestamp("2013-02-27 00:00:00"),
            302: pd.Timestamp("2013-02-28 00:00:00"),
            303: pd.Timestamp("2013-03-01 00:00:00"),
            304: pd.Timestamp("2013-03-02 00:00:00"),
            305: pd.Timestamp("2013-03-03 00:00:00"),
            306: pd.Timestamp("2013-03-04 00:00:00"),
            307: pd.Timestamp("2013-03-05 00:00:00"),
            308: pd.Timestamp("2013-03-06 00:00:00"),
            309: pd.Timestamp("2013-03-07 00:00:00"),
            310: pd.Timestamp("2013-03-08 00:00:00"),
            311: pd.Timestamp("2013-03-09 00:00:00"),
            312: pd.Timestamp("2013-03-10 00:00:00"),
            313: pd.Timestamp("2013-03-11 00:00:00"),
            314: pd.Timestamp("2013-03-12 00:00:00"),
            315: pd.Timestamp("2013-03-13 00:00:00"),
            316: pd.Timestamp("2013-03-14 00:00:00"),
            317: pd.Timestamp("2013-03-15 00:00:00"),
            318: pd.Timestamp("2013-03-16 00:00:00"),
            319: pd.Timestamp("2013-03-17 00:00:00"),
            320: pd.Timestamp("2013-03-18 00:00:00"),
            321: pd.Timestamp("2013-03-19 00:00:00"),
            322: pd.Timestamp("2013-03-20 00:00:00"),
            323: pd.Timestamp("2013-03-21 00:00:00"),
            324: pd.Timestamp("2013-03-22 00:00:00"),
            325: pd.Timestamp("2013-03-23 00:00:00"),
            326: pd.Timestamp("2013-03-24 00:00:00"),
            327: pd.Timestamp("2013-03-25 00:00:00"),
            328: pd.Timestamp("2013-03-26 00:00:00"),
            329: pd.Timestamp("2013-03-27 00:00:00"),
            330: pd.Timestamp("2013-03-28 00:00:00"),
            331: pd.Timestamp("2013-03-29 00:00:00"),
            332: pd.Timestamp("2013-03-30 00:00:00"),
            333: pd.Timestamp("2013-03-31 00:00:00"),
            334: pd.Timestamp("2013-04-01 00:00:00"),
            335: pd.Timestamp("2013-04-02 00:00:00"),
            336: pd.Timestamp("2013-04-03 00:00:00"),
            337: pd.Timestamp("2013-04-04 00:00:00"),
            338: pd.Timestamp("2013-04-05 00:00:00"),
            339: pd.Timestamp("2013-04-06 00:00:00"),
            340: pd.Timestamp("2013-04-07 00:00:00"),
            341: pd.Timestamp("2013-04-08 00:00:00"),
            342: pd.Timestamp("2013-04-09 00:00:00"),
            343: pd.Timestamp("2013-04-10 00:00:00"),
            344: pd.Timestamp("2013-04-11 00:00:00"),
            345: pd.Timestamp("2013-04-12 00:00:00"),
            346: pd.Timestamp("2013-04-13 00:00:00"),
            347: pd.Timestamp("2013-04-14 00:00:00"),
            348: pd.Timestamp("2013-04-15 00:00:00"),
            349: pd.Timestamp("2013-04-16 00:00:00"),
            350: pd.Timestamp("2013-04-17 00:00:00"),
            351: pd.Timestamp("2013-04-18 00:00:00"),
            352: pd.Timestamp("2013-04-19 00:00:00"),
            353: pd.Timestamp("2013-04-20 00:00:00"),
            354: pd.Timestamp("2013-04-21 00:00:00"),
            355: pd.Timestamp("2013-04-22 00:00:00"),
            356: pd.Timestamp("2013-04-23 00:00:00"),
            357: pd.Timestamp("2013-04-24 00:00:00"),
            358: pd.Timestamp("2013-04-25 00:00:00"),
            359: pd.Timestamp("2013-04-26 00:00:00"),
            360: pd.Timestamp("2013-04-27 00:00:00"),
            361: pd.Timestamp("2013-04-28 00:00:00"),
            362: pd.Timestamp("2013-04-29 00:00:00"),
            363: pd.Timestamp("2013-04-30 00:00:00"),
            364: pd.Timestamp("2013-05-01 00:00:00"),
            365: pd.Timestamp("2013-05-02 00:00:00"),
            366: pd.Timestamp("2013-05-03 00:00:00"),
            367: pd.Timestamp("2013-05-04 00:00:00"),
            368: pd.Timestamp("2013-05-05 00:00:00"),
            369: pd.Timestamp("2013-05-06 00:00:00"),
            370: pd.Timestamp("2013-05-07 00:00:00"),
            371: pd.Timestamp("2013-05-08 00:00:00"),
            372: pd.Timestamp("2013-05-09 00:00:00"),
            373: pd.Timestamp("2013-05-10 00:00:00"),
            374: pd.Timestamp("2013-05-11 00:00:00"),
            375: pd.Timestamp("2013-05-12 00:00:00"),
            376: pd.Timestamp("2013-05-13 00:00:00"),
            377: pd.Timestamp("2013-05-14 00:00:00"),
            378: pd.Timestamp("2013-05-15 00:00:00"),
            379: pd.Timestamp("2013-05-16 00:00:00"),
            380: pd.Timestamp("2013-05-17 00:00:00"),
            381: pd.Timestamp("2013-05-18 00:00:00"),
            382: pd.Timestamp("2013-05-19 00:00:00"),
            383: pd.Timestamp("2013-05-20 00:00:00"),
            384: pd.Timestamp("2013-05-21 00:00:00"),
            385: pd.Timestamp("2013-05-22 00:00:00"),
            386: pd.Timestamp("2013-05-23 00:00:00"),
            387: pd.Timestamp("2013-05-24 00:00:00"),
            388: pd.Timestamp("2013-05-25 00:00:00"),
            389: pd.Timestamp("2013-05-26 00:00:00"),
            390: pd.Timestamp("2013-05-27 00:00:00"),
            391: pd.Timestamp("2013-05-28 00:00:00"),
            392: pd.Timestamp("2013-05-29 00:00:00"),
            393: pd.Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 8.242579885263305,
            1: 8.234246162620314,
            2: 8.223763267886602,
            3: 8.1790740643318,
            4: 8.150311907440315,
            5: 8.382947063270414,
            6: 8.487215019931037,
            7: 8.455382878666658,
            8: 8.392413806237709,
            9: 8.291487292989727,
            10: 8.193767610737689,
            11: 8.08160740237286,
            12: 8.020863794085573,
            13: 7.977233887907018,
            14: 7.956304610638055,
            15: 7.975187454208408,
            16: 7.969961589491025,
            17: 7.923131793725949,
            18: 7.820115824923375,
            19: 7.755039199528699,
            20: 7.8112884216692455,
            21: 7.926673465860681,
            22: 7.950313059660761,
            23: 7.913263352625058,
            24: 7.878184384140694,
            25: 7.785181516690582,
            26: 7.751486816239498,
            27: 7.729348581883333,
            28: 7.750251658809953,
            29: 7.764915875302327,
            30: 7.817160071430963,
            31: 7.816146666145827,
            32: 7.779476480026709,
            33: 7.759778223782158,
            34: 7.778696507078529,
            35: 7.814521771195689,
            36: 7.776691642112765,
            37: 7.7643913744999855,
            38: 7.791167812401593,
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            382: 9.323881442246428,
            383: 9.342079291883135,
            384: 9.360039450018162,
            385: 9.377771082464085,
            386: 9.39528278066915,
            387: 9.412582610877404,
            388: 9.42967815800772,
            389: 9.44657656492907,
            390: 9.463284567708946,
            391: 9.479808527328377,
            392: 9.49615445828699,
            393: 9.512328054462964,
        },
    }
)


# Holt Winters model
AIR_FCST_HW_1 = pd.DataFrame(
    [
        {"time": Timestamp("1949-01-01 00:00:00"), "fcst": 126.84753981623055},
        {"time": Timestamp("1949-02-01 00:00:00"), "fcst": 116.43521901797152},
        {"time": Timestamp("1949-03-01 00:00:00"), "fcst": 116.58612705161451},
        {"time": Timestamp("1949-04-01 00:00:00"), "fcst": 139.4030218435132},
        {"time": Timestamp("1949-05-01 00:00:00"), "fcst": 127.31785353777076},
        {"time": Timestamp("1949-06-01 00:00:00"), "fcst": 130.39730055826365},
        {"time": Timestamp("1949-07-01 00:00:00"), "fcst": 135.6463384943683},
        {"time": Timestamp("1949-08-01 00:00:00"), "fcst": 147.43830745237648},
        {"time": Timestamp("1949-09-01 00:00:00"), "fcst": 152.43521898003843},
        {"time": Timestamp("1949-10-01 00:00:00"), "fcst": 134.58612710330593},
        {"time": Timestamp("1949-11-01 00:00:00"), "fcst": 126.40302192145649},
        {"time": Timestamp("1949-12-01 00:00:00"), "fcst": 102.3178535567539},
        {"time": Timestamp("1950-01-01 00:00:00"), "fcst": 127.39730048736035},
        {"time": Timestamp("1950-02-01 00:00:00"), "fcst": 115.64633852952926},
        {"time": Timestamp("1950-03-01 00:00:00"), "fcst": 125.43830744367528},
        {"time": Timestamp("1950-04-01 00:00:00"), "fcst": 145.43521892783133},
        {"time": Timestamp("1950-05-01 00:00:00"), "fcst": 133.58612707914088},
        {"time": Timestamp("1950-06-01 00:00:00"), "fcst": 132.40302189693048},
        {"time": Timestamp("1950-07-01 00:00:00"), "fcst": 147.31785345420252},
        {"time": Timestamp("1950-08-01 00:00:00"), "fcst": 179.39730047936334},
        {"time": Timestamp("1950-09-01 00:00:00"), "fcst": 170.64633853411505},
        {"time": Timestamp("1950-10-01 00:00:00"), "fcst": 157.43830751477844},
        {"time": Timestamp("1950-11-01 00:00:00"), "fcst": 137.43521903345732},
        {"time": Timestamp("1950-12-01 00:00:00"), "fcst": 112.58612710243753},
        {"time": Timestamp("1951-01-01 00:00:00"), "fcst": 147.40302179222792},
        {"time": Timestamp("1951-02-01 00:00:00"), "fcst": 143.317853500865},
        {"time": Timestamp("1951-03-01 00:00:00"), "fcst": 159.39730051164796},
        {"time": Timestamp("1951-04-01 00:00:00"), "fcst": 178.64633844990703},
        {"time": Timestamp("1951-05-01 00:00:00"), "fcst": 162.43830751865534},
        {"time": Timestamp("1951-06-01 00:00:00"), "fcst": 176.43521894313096},
        {"time": Timestamp("1951-07-01 00:00:00"), "fcst": 176.5861270469804},
        {"time": Timestamp("1951-08-01 00:00:00"), "fcst": 206.40302182126086},
        {"time": Timestamp("1951-09-01 00:00:00"), "fcst": 197.31785352810135},
        {"time": Timestamp("1951-10-01 00:00:00"), "fcst": 193.39730057484107},
        {"time": Timestamp("1951-11-01 00:00:00"), "fcst": 162.64633858142048},
        {"time": Timestamp("1951-12-01 00:00:00"), "fcst": 145.43830750887463},
        {"time": Timestamp("1952-01-01 00:00:00"), "fcst": 170.43521890278816},
        {"time": Timestamp("1952-02-01 00:00:00"), "fcst": 169.5861270407929},
        {"time": Timestamp("1952-03-01 00:00:00"), "fcst": 187.4030218423999},
        {"time": Timestamp("1952-04-01 00:00:00"), "fcst": 191.31785348200606},
        {"time": Timestamp("1952-05-01 00:00:00"), "fcst": 190.397300558586},
        {"time": Timestamp("1952-06-01 00:00:00"), "fcst": 183.64633851338857},
        {"time": Timestamp("1952-07-01 00:00:00"), "fcst": 217.4383073815103},
        {"time": Timestamp("1952-08-01 00:00:00"), "fcst": 234.43521894224637},
        {"time": Timestamp("1952-09-01 00:00:00"), "fcst": 240.58612703962305},
        {"time": Timestamp("1952-10-01 00:00:00"), "fcst": 216.40302196707333},
        {"time": Timestamp("1952-11-01 00:00:00"), "fcst": 189.3178535639019},
        {"time": Timestamp("1952-12-01 00:00:00"), "fcst": 181.39730056893725},
        {"time": Timestamp("1953-01-01 00:00:00"), "fcst": 194.64633845326824},
        {"time": Timestamp("1953-02-01 00:00:00"), "fcst": 195.43830746197204},
        {"time": Timestamp("1953-03-01 00:00:00"), "fcst": 200.43521895799557},
        {"time": Timestamp("1953-04-01 00:00:00"), "fcst": 234.58612695038445},
        {"time": Timestamp("1953-05-01 00:00:00"), "fcst": 242.4030218778282},
        {"time": Timestamp("1953-06-01 00:00:00"), "fcst": 227.3178535364932},
        {"time": Timestamp("1953-07-01 00:00:00"), "fcst": 252.39730049298564},
        {"time": Timestamp("1953-08-01 00:00:00"), "fcst": 264.6463384747489},
        {"time": Timestamp("1953-09-01 00:00:00"), "fcst": 271.43830746547513},
        {"time": Timestamp("1953-10-01 00:00:00"), "fcst": 241.43521906684725},
        {"time": Timestamp("1953-11-01 00:00:00"), "fcst": 209.5861271246836},
        {"time": Timestamp("1953-12-01 00:00:00"), "fcst": 187.40302193780357},
        {"time": Timestamp("1954-01-01 00:00:00"), "fcst": 199.31785344222556},
        {"time": Timestamp("1954-02-01 00:00:00"), "fcst": 213.39730050406197},
        {"time": Timestamp("1954-03-01 00:00:00"), "fcst": 188.64633854731198},
        {"time": Timestamp("1954-04-01 00:00:00"), "fcst": 234.4383073334589},
        {"time": Timestamp("1954-05-01 00:00:00"), "fcst": 231.4352189790476},
        {"time": Timestamp("1954-06-01 00:00:00"), "fcst": 232.58612703148432},
        {"time": Timestamp("1954-07-01 00:00:00"), "fcst": 271.4030217859889},
        {"time": Timestamp("1954-08-01 00:00:00"), "fcst": 300.3178534230733},
        {"time": Timestamp("1954-09-01 00:00:00"), "fcst": 302.3973005640082},
        {"time": Timestamp("1954-10-01 00:00:00"), "fcst": 259.64633861804765},
        {"time": Timestamp("1954-11-01 00:00:00"), "fcst": 228.43830754622365},
        {"time": Timestamp("1954-12-01 00:00:00"), "fcst": 207.43521901564736},
        {"time": Timestamp("1955-01-01 00:00:00"), "fcst": 227.58612696849107},
        {"time": Timestamp("1955-02-01 00:00:00"), "fcst": 249.40302182043112},
        {"time": Timestamp("1955-03-01 00:00:00"), "fcst": 231.3178535269139},
        {"time": Timestamp("1955-04-01 00:00:00"), "fcst": 276.3973004244398},
        {"time": Timestamp("1955-05-01 00:00:00"), "fcst": 269.64633851154855},
        {"time": Timestamp("1955-06-01 00:00:00"), "fcst": 269.43830746524475},
        {"time": Timestamp("1955-07-01 00:00:00"), "fcst": 319.43521884513166},
        {"time": Timestamp("1955-08-01 00:00:00"), "fcst": 362.5861269442986},
        {"time": Timestamp("1955-09-01 00:00:00"), "fcst": 354.40302193789864},
        {"time": Timestamp("1955-10-01 00:00:00"), "fcst": 310.317853623534},
        {"time": Timestamp("1955-11-01 00:00:00"), "fcst": 283.3973006274947},
        {"time": Timestamp("1955-12-01 00:00:00"), "fcst": 237.64633860567437},
        {"time": Timestamp("1956-01-01 00:00:00"), "fcst": 277.4383073465219},
        {"time": Timestamp("1956-02-01 00:00:00"), "fcst": 288.4352189396168},
        {"time": Timestamp("1956-03-01 00:00:00"), "fcst": 275.5861270675572},
        {"time": Timestamp("1956-04-01 00:00:00"), "fcst": 324.4030217566258},
        {"time": Timestamp("1956-05-01 00:00:00"), "fcst": 311.317853528182},
        {"time": Timestamp("1956-06-01 00:00:00"), "fcst": 327.3973005130789},
        {"time": Timestamp("1956-07-01 00:00:00"), "fcst": 374.6463383808649},
        {"time": Timestamp("1956-08-01 00:00:00"), "fcst": 412.43830739423504},
        {"time": Timestamp("1956-09-01 00:00:00"), "fcst": 409.43521901681396},
        {"time": Timestamp("1956-10-01 00:00:00"), "fcst": 353.5861272127139},
        {"time": Timestamp("1956-11-01 00:00:00"), "fcst": 313.4030220021086},
        {"time": Timestamp("1956-12-01 00:00:00"), "fcst": 269.31785359570483},
        {"time": Timestamp("1957-01-01 00:00:00"), "fcst": 315.397300415932},
        {"time": Timestamp("1957-02-01 00:00:00"), "fcst": 315.64633848829874},
        {"time": Timestamp("1957-03-01 00:00:00"), "fcst": 300.4383074998819},
        {"time": Timestamp("1957-04-01 00:00:00"), "fcst": 360.43521881145654},
        {"time": Timestamp("1957-05-01 00:00:00"), "fcst": 346.58612708284875},
        {"time": Timestamp("1957-06-01 00:00:00"), "fcst": 362.4030218520986},
        {"time": Timestamp("1957-07-01 00:00:00"), "fcst": 420.31785335003985},
        {"time": Timestamp("1957-08-01 00:00:00"), "fcst": 474.39730044350654},
        {"time": Timestamp("1957-09-01 00:00:00"), "fcst": 467.6463385565014},
        {"time": Timestamp("1957-10-01 00:00:00"), "fcst": 403.43830767127895},
        {"time": Timestamp("1957-11-01 00:00:00"), "fcst": 351.4352191238112},
        {"time": Timestamp("1957-12-01 00:00:00"), "fcst": 303.5861271586409},
        {"time": Timestamp("1958-01-01 00:00:00"), "fcst": 343.4030217696936},
        {"time": Timestamp("1958-02-01 00:00:00"), "fcst": 338.31785349522875},
        {"time": Timestamp("1958-03-01 00:00:00"), "fcst": 327.3973005710917},
        {"time": Timestamp("1958-04-01 00:00:00"), "fcst": 362.64633839142243},
        {"time": Timestamp("1958-05-01 00:00:00"), "fcst": 347.4383075036734},
        {"time": Timestamp("1958-06-01 00:00:00"), "fcst": 367.4352189159238},
        {"time": Timestamp("1958-07-01 00:00:00"), "fcst": 433.58612687091556},
        {"time": Timestamp("1958-08-01 00:00:00"), "fcst": 498.40302174693534},
        {"time": Timestamp("1958-09-01 00:00:00"), "fcst": 503.3178535192069},
        {"time": Timestamp("1958-10-01 00:00:00"), "fcst": 413.3973008226669},
        {"time": Timestamp("1958-11-01 00:00:00"), "fcst": 359.64633863977986},
        {"time": Timestamp("1958-12-01 00:00:00"), "fcst": 309.43830758413844},
        {"time": Timestamp("1959-01-01 00:00:00"), "fcst": 341.4352188655183},
        {"time": Timestamp("1959-02-01 00:00:00"), "fcst": 358.58612697835247},
        {"time": Timestamp("1959-03-01 00:00:00"), "fcst": 349.4030218996877},
        {"time": Timestamp("1959-04-01 00:00:00"), "fcst": 404.3178533332741},
        {"time": Timestamp("1959-05-01 00:00:00"), "fcst": 405.39730055150756},
        {"time": Timestamp("1959-06-01 00:00:00"), "fcst": 420.64633845669096},
        {"time": Timestamp("1959-07-01 00:00:00"), "fcst": 471.4383073453023},
        {"time": Timestamp("1959-08-01 00:00:00"), "fcst": 552.4352187938528},
        {"time": Timestamp("1959-09-01 00:00:00"), "fcst": 557.5861270778247},
        {"time": Timestamp("1959-10-01 00:00:00"), "fcst": 470.40302216096313},
        {"time": Timestamp("1959-11-01 00:00:00"), "fcst": 405.31785367223165},
        {"time": Timestamp("1959-12-01 00:00:00"), "fcst": 371.39730063340994},
        {"time": Timestamp("1960-01-01 00:00:00"), "fcst": 405.64633839010435},
        {"time": Timestamp("1960-02-01 00:00:00"), "fcst": 416.43830743403987},
        {"time": Timestamp("1960-03-01 00:00:00"), "fcst": 395.435219024265},
        {"time": Timestamp("1960-04-01 00:00:00"), "fcst": 417.5861269720748},
        {"time": Timestamp("1960-05-01 00:00:00"), "fcst": 468.4030217567773},
        {"time": Timestamp("1960-06-01 00:00:00"), "fcst": 470.31785349633265},
        {"time": Timestamp("1960-07-01 00:00:00"), "fcst": 544.3973003783929},
        {"time": Timestamp("1960-08-01 00:00:00"), "fcst": 622.646338334976},
        {"time": Timestamp("1960-09-01 00:00:00"), "fcst": 605.4383075759382},
        {"time": Timestamp("1960-10-01 00:00:00"), "fcst": 512.4352192668457},
        {"time": Timestamp("1960-11-01 00:00:00"), "fcst": 459.5861271963348},
        {"time": Timestamp("1960-12-01 00:00:00"), "fcst": 397.40302204973},
        # {"time": NaT, "fcst": 430.3178533862482},
        {"time": Timestamp("1961-01-01 00:00:00"), "fcst": 430.3178533862482},
        {"time": Timestamp("1961-02-01 00:00:00"), "fcst": 439.7151539065179},
        {"time": Timestamp("1961-03-01 00:00:00"), "fcst": 440.3614923926484},
        {"time": Timestamp("1961-04-01 00:00:00"), "fcst": 439.7997998546415},
        {"time": Timestamp("1961-05-01 00:00:00"), "fcst": 444.23501881017836},
        {"time": Timestamp("1961-06-01 00:00:00"), "fcst": 442.8211458460015},
        {"time": Timestamp("1961-07-01 00:00:00"), "fcst": 450.22416770801783},
        {"time": Timestamp("1961-08-01 00:00:00"), "fcst": 448.54202119377453},
        {"time": Timestamp("1961-09-01 00:00:00"), "fcst": 457.93932171404424},
        {"time": Timestamp("1961-10-01 00:00:00"), "fcst": 458.58566020017474},
        {"time": Timestamp("1961-11-01 00:00:00"), "fcst": 458.0239676621678},
        {"time": Timestamp("1961-12-01 00:00:00"), "fcst": 462.4591866177047},
        {"time": Timestamp("1962-01-01 00:00:00"), "fcst": 461.04531365352784},
        {"time": Timestamp("1962-02-01 00:00:00"), "fcst": 468.44833551554416},
        {"time": Timestamp("1962-03-01 00:00:00"), "fcst": 466.766189001301},
        {"time": Timestamp("1962-04-01 00:00:00"), "fcst": 476.1634895215707},
        {"time": Timestamp("1962-05-01 00:00:00"), "fcst": 476.8098280077012},
        {"time": Timestamp("1962-06-01 00:00:00"), "fcst": 476.24813546969426},
        {"time": Timestamp("1962-07-01 00:00:00"), "fcst": 480.68335442523113},
        {"time": Timestamp("1962-08-01 00:00:00"), "fcst": 479.2694814610543},
        {"time": Timestamp("1962-09-01 00:00:00"), "fcst": 486.6725033230705},
        {"time": Timestamp("1962-10-01 00:00:00"), "fcst": 484.9903568088273},
        {"time": Timestamp("1962-11-01 00:00:00"), "fcst": 494.387657329097},
        {"time": Timestamp("1962-12-01 00:00:00"), "fcst": 495.0339958152275},
        {"time": Timestamp("1963-01-01 00:00:00"), "fcst": 494.4723032772206},
        {"time": Timestamp("1963-02-01 00:00:00"), "fcst": 498.90752223275746},
        {"time": Timestamp("1963-03-01 00:00:00"), "fcst": 497.4936492685806},
        {"time": Timestamp("1963-04-01 00:00:00"), "fcst": 504.8966711305969},
        {"time": Timestamp("1963-05-01 00:00:00"), "fcst": 503.21452461635374},
        {"time": Timestamp("1963-06-01 00:00:00"), "fcst": 512.6118251366235},
    ]
)

AIR_FCST_HW_2 = pd.DataFrame(
    {
        "time": {
            144: Timestamp("1960-12-02 00:00:00"),
            145: Timestamp("1960-12-03 00:00:00"),
            146: Timestamp("1960-12-04 00:00:00"),
            147: Timestamp("1960-12-05 00:00:00"),
            148: Timestamp("1960-12-06 00:00:00"),
            149: Timestamp("1960-12-07 00:00:00"),
            150: Timestamp("1960-12-08 00:00:00"),
            151: Timestamp("1960-12-09 00:00:00"),
            152: Timestamp("1960-12-10 00:00:00"),
            153: Timestamp("1960-12-11 00:00:00"),
            154: Timestamp("1960-12-12 00:00:00"),
            155: Timestamp("1960-12-13 00:00:00"),
            156: Timestamp("1960-12-14 00:00:00"),
            157: Timestamp("1960-12-15 00:00:00"),
            158: Timestamp("1960-12-16 00:00:00"),
            159: Timestamp("1960-12-17 00:00:00"),
            160: Timestamp("1960-12-18 00:00:00"),
            161: Timestamp("1960-12-19 00:00:00"),
            162: Timestamp("1960-12-20 00:00:00"),
            163: Timestamp("1960-12-21 00:00:00"),
            164: Timestamp("1960-12-22 00:00:00"),
            165: Timestamp("1960-12-23 00:00:00"),
            166: Timestamp("1960-12-24 00:00:00"),
            167: Timestamp("1960-12-25 00:00:00"),
            168: Timestamp("1960-12-26 00:00:00"),
            169: Timestamp("1960-12-27 00:00:00"),
            170: Timestamp("1960-12-28 00:00:00"),
            171: Timestamp("1960-12-29 00:00:00"),
            172: Timestamp("1960-12-30 00:00:00"),
            173: Timestamp("1960-12-31 00:00:00"),
        },
        "fcst": {
            144: 426.91782612904683,
            145: 442.61178390165213,
            146: 432.030759167949,
            147: 429.6691445287656,
            148: 434.9990742994176,
            149: 429.66385535629826,
            150: 447.1960753762643,
            151: 444.76249371560476,
            152: 461.1124406327931,
            153: 450.089141396734,
            154: 447.6288140179448,
            155: 453.18152864131514,
            156: 447.6233037642325,
            157: 465.8883501483431,
            158: 463.3530476104531,
            159: 480.3864032539542,
            160: 468.9023429567837,
            161: 466.3391767609165,
            162: 472.12398838420717,
            163: 466.33343618502084,
            164: 485.3619402212348,
            165: 482.72066499199514,
            166: 500.4659950500994,
            167: 488.5019144164508,
            168: 485.83161086080395,
            169: 491.85822086811726,
            170: 485.8256303355261,
            171: 505.6495036639397,
            172: 502.8978262083566,
            173: 521.3848903818335,
        },
        "fcst_lower": {
            144: 560.9970638278712,
            145: 577.9179099326489,
            146: 568.5637735311182,
            147: 567.4290472241071,
            148: 573.9858653269314,
            149: 569.8775347159844,
            150: 588.6366430681228,
            151: 587.4299497396356,
            152: 605.0067849889964,
            153: 595.2103740851096,
            154: 593.9769350384928,
            155: 600.7565379940355,
            156: 596.4252014491252,
            157: 615.9171361654081,
            158: 614.6087219596905,
            159: 632.868965935364,
            160: 622.6117939703659,
            161: 621.2755161066709,
            162: 628.287216062134,
            163: 623.7235521951201,
            164: 643.9789445635064,
            165: 642.5645576664391,
            166: 661.5367760567157,
            167: 650.7995837552394,
            168: 649.356168531765,
            169: 656.6096668712506,
            170: 651.8039646708319,
            171: 672.8547263314177,
            172: 671.329937208007,
            173: 691.0438897136563,
        },
        "fcst_upper": {
            144: 292.83858843022244,
            145: 307.3056578706554,
            146: 295.49774480477987,
            147: 291.9092418334242,
            148: 296.01228327190375,
            149: 289.4501759966121,
            150: 305.75550768440576,
            151: 302.0950376915739,
            152: 317.2180962765898,
            153: 304.96790870835844,
            154: 301.28069299739684,
            155: 305.60651928859477,
            156: 298.8214060793398,
            157: 315.85956413127803,
            158: 312.0973732612157,
            159: 327.9038405725444,
            160: 315.1928919432015,
            161: 311.40283741516197,
            162: 315.9607607062803,
            163: 308.9433201749216,
            164: 326.74493587896325,
            165: 322.8767723175512,
            166: 339.3952140434832,
            167: 326.20424507766216,
            168: 322.3070531898429,
            169: 327.1067748649839,
            170: 319.8472960002204,
            171: 338.44428099646166,
            172: 334.46571520870623,
            173: 351.7258910500108,
        },
    }
)

AIR_FCST_15_LSTM_PARAM_1_MODEL_1_MONTHLY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("1961-01-01 00:00:00"),
            1: Timestamp("1961-02-01 00:00:00"),
            2: Timestamp("1961-03-01 00:00:00"),
            3: Timestamp("1961-04-01 00:00:00"),
            4: Timestamp("1961-05-01 00:00:00"),
            5: Timestamp("1961-06-01 00:00:00"),
            6: Timestamp("1961-07-01 00:00:00"),
            7: Timestamp("1961-08-01 00:00:00"),
            8: Timestamp("1961-09-01 00:00:00"),
            9: Timestamp("1961-10-01 00:00:00"),
            10: Timestamp("1961-11-01 00:00:00"),
            11: Timestamp("1961-12-01 00:00:00"),
            12: Timestamp("1962-01-01 00:00:00"),
            13: Timestamp("1962-02-01 00:00:00"),
            14: Timestamp("1962-03-01 00:00:00"),
        },
        "fcst": {
            0: 439.06164887547493,
            1: 437.9799669086933,
            2: 436.5363580584526,
            3: 435.0198451280594,
            4: 434.1370380818844,
            5: 433.70533323287964,
            6: 434.0240734219551,
            7: 433.92943319678307,
            8: 433.791150867939,
            9: 433.6823003292084,
            10: 433.60224092006683,
            11: 433.54815527796745,
            12: 433.5110664367676,
            13: 433.4845832288265,
            14: 433.4599988460541,
        },
        "fcst_lower": {
            0: 417.10856643170115,
            1: 416.08096856325864,
            2: 414.70954015552996,
            3: 413.2688528716564,
            4: 412.4301861777901,
            5: 412.02006657123565,
            6: 412.3228697508573,
            7: 412.2329615369439,
            8: 412.101593324542,
            9: 411.9981853127479,
            10: 411.92212887406345,
            11: 411.8707475140691,
            12: 411.8355131149292,
            13: 411.81035406738516,
            14: 411.78699890375134,
        },
        "fcst_upper": {
            0: 461.0147313192487,
            1: 459.878965254128,
            2: 458.36317596137525,
            3: 456.77083738446237,
            4: 455.84388998597865,
            5: 455.39059989452363,
            6: 455.7252770930529,
            7: 455.62590485662224,
            8: 455.48070841133597,
            9: 455.3664153456688,
            10: 455.2823529660702,
            11: 455.2255630418658,
            12: 455.18661975860596,
            13: 455.1588123902679,
            14: 455.1329987883568,
        },
    }
)

AIR_FCST_15_LSTM_PARAM_2_MODEL_1_MONTHLY = pd.DataFrame(
    [
        {
            "time": pd.Timestamp("1961-01-01 00:00:00"),
            "fcst": 422.1656014919281,
            "fcst_lower": 401.05732141733165,
            "fcst_upper": 443.27388156652455,
        },
        {
            "time": pd.Timestamp("1961-02-01 00:00:00"),
            "fcst": 417.3987707197666,
            "fcst_lower": 396.52883218377826,
            "fcst_upper": 438.26870925575497,
        },
        {
            "time": pd.Timestamp("1961-03-01 00:00:00"),
            "fcst": 420.79761373996735,
            "fcst_lower": 399.75773305296894,
            "fcst_upper": 441.83749442696575,
        },
        {
            "time": pd.Timestamp("1961-04-01 00:00:00"),
            "fcst": 419.23954278230667,
            "fcst_lower": 398.27756564319134,
            "fcst_upper": 440.201519921422,
        },
        {
            "time": pd.Timestamp("1961-05-01 00:00:00"),
            "fcst": 418.8230516910553,
            "fcst_lower": 397.8818991065025,
            "fcst_upper": 439.7642042756081,
        },
        {
            "time": pd.Timestamp("1961-06-01 00:00:00"),
            "fcst": 418.9471893161535,
            "fcst_lower": 397.9998298503458,
            "fcst_upper": 439.89454878196125,
        },
        {
            "time": pd.Timestamp("1961-07-01 00:00:00"),
            "fcst": 418.66706243157387,
            "fcst_lower": 397.73370930999516,
            "fcst_upper": 439.6004155531526,
        },
        {
            "time": pd.Timestamp("1961-08-01 00:00:00"),
            "fcst": 418.5670383423567,
            "fcst_lower": 397.6386864252388,
            "fcst_upper": 439.49539025947456,
        },
        {
            "time": pd.Timestamp("1961-09-01 00:00:00"),
            "fcst": 418.5116983950138,
            "fcst_lower": 397.5861134752631,
            "fcst_upper": 439.4372833147645,
        },
        {
            "time": pd.Timestamp("1961-10-01 00:00:00"),
            "fcst": 418.43945813179016,
            "fcst_lower": 397.51748522520063,
            "fcst_upper": 439.3614310383797,
        },
        {
            "time": pd.Timestamp("1961-11-01 00:00:00"),
            "fcst": 418.3985098898411,
            "fcst_lower": 397.478584395349,
            "fcst_upper": 439.31843538433316,
        },
        {
            "time": pd.Timestamp("1961-12-01 00:00:00"),
            "fcst": 418.3679087013006,
            "fcst_lower": 397.44951326623556,
            "fcst_upper": 439.2863041363657,
        },
        {
            "time": pd.Timestamp("1962-01-01 00:00:00"),
            "fcst": 418.3428727686405,
            "fcst_lower": 397.4257291302085,
            "fcst_upper": 439.26001640707256,
        },
        {
            "time": pd.Timestamp("1962-02-01 00:00:00"),
            "fcst": 418.3256096690893,
            "fcst_lower": 397.4093291856348,
            "fcst_upper": 439.2418901525438,
        },
        {
            "time": pd.Timestamp("1962-03-01 00:00:00"),
            "fcst": 418.31263050436974,
            "fcst_lower": 397.39699897915125,
            "fcst_upper": 439.2282620295882,
        },
    ]
)

AIR_FCST_30_LSTM_PARAM_1_MODEL_1_MONTHLY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("1961-01-01 00:00:00"),
            1: Timestamp("1961-02-01 00:00:00"),
            2: Timestamp("1961-03-01 00:00:00"),
            3: Timestamp("1961-04-01 00:00:00"),
            4: Timestamp("1961-05-01 00:00:00"),
            5: Timestamp("1961-06-01 00:00:00"),
            6: Timestamp("1961-07-01 00:00:00"),
            7: Timestamp("1961-08-01 00:00:00"),
            8: Timestamp("1961-09-01 00:00:00"),
            9: Timestamp("1961-10-01 00:00:00"),
            10: Timestamp("1961-11-01 00:00:00"),
            11: Timestamp("1961-12-01 00:00:00"),
            12: Timestamp("1962-01-01 00:00:00"),
            13: Timestamp("1962-02-01 00:00:00"),
            14: Timestamp("1962-03-01 00:00:00"),
            15: Timestamp("1962-04-01 00:00:00"),
            16: Timestamp("1962-05-01 00:00:00"),
            17: Timestamp("1962-06-01 00:00:00"),
            18: Timestamp("1962-07-01 00:00:00"),
            19: Timestamp("1962-08-01 00:00:00"),
            20: Timestamp("1962-09-01 00:00:00"),
            21: Timestamp("1962-10-01 00:00:00"),
            22: Timestamp("1962-11-01 00:00:00"),
            23: Timestamp("1962-12-01 00:00:00"),
            24: Timestamp("1963-01-01 00:00:00"),
            25: Timestamp("1963-02-01 00:00:00"),
            26: Timestamp("1963-03-01 00:00:00"),
            27: Timestamp("1963-04-01 00:00:00"),
            28: Timestamp("1963-05-01 00:00:00"),
            29: Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 439.06164887547493,
            1: 437.9799669086933,
            2: 436.5363580584526,
            3: 435.0198451280594,
            4: 434.1370380818844,
            5: 433.70533323287964,
            6: 434.0240734219551,
            7: 433.92943319678307,
            8: 433.791150867939,
            9: 433.6823003292084,
            10: 433.60224092006683,
            11: 433.54815527796745,
            12: 433.5110664367676,
            13: 433.4845832288265,
            14: 433.4599988460541,
            15: 433.4403776526451,
            16: 433.4252333641052,
            17: 433.4136242866516,
            18: 433.40467047691345,
            19: 433.3977235555649,
            20: 433.392304956913,
            21: 433.3880287408829,
            22: 433.38470193743706,
            23: 433.3821084201336,
            24: 433.3800783753395,
            25: 433.37851917743683,
            26: 433.3772841691971,
            27: 433.3763347566128,
            28: 433.37558603286743,
            29: 433.37500712275505,
        },
        "fcst_lower": {
            0: 417.10856643170115,
            1: 416.08096856325864,
            2: 414.70954015552996,
            3: 413.2688528716564,
            4: 412.4301861777901,
            5: 412.02006657123565,
            6: 412.3228697508573,
            7: 412.2329615369439,
            8: 412.101593324542,
            9: 411.9981853127479,
            10: 411.92212887406345,
            11: 411.8707475140691,
            12: 411.8355131149292,
            13: 411.81035406738516,
            14: 411.78699890375134,
            15: 411.7683587700128,
            16: 411.75397169589996,
            17: 411.742943072319,
            18: 411.73443695306776,
            19: 411.7278373777866,
            20: 411.7226897090673,
            21: 411.71862730383873,
            22: 411.71546684056517,
            23: 411.7130029991269,
            24: 411.71107445657253,
            25: 411.709593218565,
            26: 411.7084199607372,
            27: 411.70751801878214,
            28: 411.70680673122405,
            29: 411.7062567666173,
        },
        "fcst_upper": {
            0: 461.0147313192487,
            1: 459.878965254128,
            2: 458.36317596137525,
            3: 456.77083738446237,
            4: 455.84388998597865,
            5: 455.39059989452363,
            6: 455.7252770930529,
            7: 455.62590485662224,
            8: 455.48070841133597,
            9: 455.3664153456688,
            10: 455.2823529660702,
            11: 455.2255630418658,
            12: 455.18661975860596,
            13: 455.1588123902679,
            14: 455.1329987883568,
            15: 455.1123965352774,
            16: 455.0964950323105,
            17: 455.0843055009842,
            18: 455.07490400075915,
            19: 455.06760973334315,
            20: 455.0619202047587,
            21: 455.057430177927,
            22: 455.05393703430894,
            23: 455.0512138411403,
            24: 455.0490822941065,
            25: 455.0474451363087,
            26: 455.04614837765695,
            27: 455.0451514944434,
            28: 455.0443653345108,
            29: 455.0437574788928,
        },
    }
)

AIR_FCST_30_LSTM_PARAM_2_MODEL_1_MONTHLY = pd.DataFrame(
    [
        {
            "time": Timestamp("1961-01-01 00:00:00"),
            "fcst": 422.1656014919281,
            "fcst_lower": 401.05732141733165,
            "fcst_upper": 443.27388156652455,
        },
        {
            "time": Timestamp("1961-02-01 00:00:00"),
            "fcst": 417.3987707197666,
            "fcst_lower": 396.52883218377826,
            "fcst_upper": 438.26870925575497,
        },
        {
            "time": Timestamp("1961-03-01 00:00:00"),
            "fcst": 420.79761373996735,
            "fcst_lower": 399.75773305296894,
            "fcst_upper": 441.83749442696575,
        },
        {
            "time": Timestamp("1961-04-01 00:00:00"),
            "fcst": 419.23954278230667,
            "fcst_lower": 398.27756564319134,
            "fcst_upper": 440.201519921422,
        },
        {
            "time": Timestamp("1961-05-01 00:00:00"),
            "fcst": 418.8230516910553,
            "fcst_lower": 397.8818991065025,
            "fcst_upper": 439.7642042756081,
        },
        {
            "time": Timestamp("1961-06-01 00:00:00"),
            "fcst": 418.9471893161535,
            "fcst_lower": 397.9998298503458,
            "fcst_upper": 439.89454878196125,
        },
        {
            "time": Timestamp("1961-07-01 00:00:00"),
            "fcst": 418.66706243157387,
            "fcst_lower": 397.73370930999516,
            "fcst_upper": 439.6004155531526,
        },
        {
            "time": Timestamp("1961-08-01 00:00:00"),
            "fcst": 418.5670383423567,
            "fcst_lower": 397.6386864252388,
            "fcst_upper": 439.49539025947456,
        },
        {
            "time": Timestamp("1961-09-01 00:00:00"),
            "fcst": 418.5116983950138,
            "fcst_lower": 397.5861134752631,
            "fcst_upper": 439.4372833147645,
        },
        {
            "time": Timestamp("1961-10-01 00:00:00"),
            "fcst": 418.43945813179016,
            "fcst_lower": 397.51748522520063,
            "fcst_upper": 439.3614310383797,
        },
        {
            "time": Timestamp("1961-11-01 00:00:00"),
            "fcst": 418.3985098898411,
            "fcst_lower": 397.478584395349,
            "fcst_upper": 439.31843538433316,
        },
        {
            "time": Timestamp("1961-12-01 00:00:00"),
            "fcst": 418.3679087013006,
            "fcst_lower": 397.44951326623556,
            "fcst_upper": 439.2863041363657,
        },
        {
            "time": Timestamp("1962-01-01 00:00:00"),
            "fcst": 418.3428727686405,
            "fcst_lower": 397.4257291302085,
            "fcst_upper": 439.26001640707256,
        },
        {
            "time": Timestamp("1962-02-01 00:00:00"),
            "fcst": 418.3256096690893,
            "fcst_lower": 397.4093291856348,
            "fcst_upper": 439.2418901525438,
        },
        {
            "time": Timestamp("1962-03-01 00:00:00"),
            "fcst": 418.31263050436974,
            "fcst_lower": 397.39699897915125,
            "fcst_upper": 439.2282620295882,
        },
        {
            "time": Timestamp("1962-04-01 00:00:00"),
            "fcst": 418.30286622047424,
            "fcst_lower": 397.38772290945053,
            "fcst_upper": 439.21800953149796,
        },
        {
            "time": Timestamp("1962-05-01 00:00:00"),
            "fcst": 418.2957417666912,
            "fcst_lower": 397.3809546783566,
            "fcst_upper": 439.2105288550258,
        },
        {
            "time": Timestamp("1962-06-01 00:00:00"),
            "fcst": 418.2904389500618,
            "fcst_lower": 397.3759170025587,
            "fcst_upper": 439.2049608975649,
        },
        {
            "time": Timestamp("1962-07-01 00:00:00"),
            "fcst": 418.2865023612976,
            "fcst_lower": 397.3721772432327,
            "fcst_upper": 439.2008274793625,
        },
        {
            "time": Timestamp("1962-08-01 00:00:00"),
            "fcst": 418.2835923731327,
            "fcst_lower": 397.36941275447606,
            "fcst_upper": 439.19777199178935,
        },
        {
            "time": Timestamp("1962-09-01 00:00:00"),
            "fcst": 418.2814349681139,
            "fcst_lower": 397.3673632197082,
            "fcst_upper": 439.1955067165196,
        },
        {
            "time": Timestamp("1962-10-01 00:00:00"),
            "fcst": 418.279833316803,
            "fcst_lower": 397.3658416509628,
            "fcst_upper": 439.19382498264315,
        },
        {
            "time": Timestamp("1962-11-01 00:00:00"),
            "fcst": 418.278648480773,
            "fcst_lower": 397.3647160567343,
            "fcst_upper": 439.1925809048116,
        },
        {
            "time": Timestamp("1962-12-01 00:00:00"),
            "fcst": 418.27776853740215,
            "fcst_lower": 397.363880110532,
            "fcst_upper": 439.1916569642723,
        },
        {
            "time": Timestamp("1963-01-01 00:00:00"),
            "fcst": 418.2771201580763,
            "fcst_lower": 397.3632641501724,
            "fcst_upper": 439.19097616598015,
        },
        {
            "time": Timestamp("1963-02-01 00:00:00"),
            "fcst": 418.27663001418114,
            "fcst_lower": 397.36279851347206,
            "fcst_upper": 439.1904615148902,
        },
        {
            "time": Timestamp("1963-03-01 00:00:00"),
            "fcst": 418.27627880871296,
            "fcst_lower": 397.3624648682773,
            "fcst_upper": 439.1900927491486,
        },
        {
            "time": Timestamp("1963-04-01 00:00:00"),
            "fcst": 418.27601251006126,
            "fcst_lower": 397.3622118845582,
            "fcst_upper": 439.18981313556435,
        },
        {
            "time": Timestamp("1963-05-01 00:00:00"),
            "fcst": 418.2758195400238,
            "fcst_lower": 397.3620285630226,
            "fcst_upper": 439.189610517025,
        },
        {
            "time": Timestamp("1963-06-01 00:00:00"),
            "fcst": 418.27567288279533,
            "fcst_lower": 397.3618892386555,
            "fcst_upper": 439.18945652693515,
        },
    ]
)

AIR_FCST_15_LSTM_PARAM_1_MODEL_2_MONTHLY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("1961-01-01 00:00:00"),
            1: Timestamp("1961-02-01 00:00:00"),
            2: Timestamp("1961-03-01 00:00:00"),
            3: Timestamp("1961-04-01 00:00:00"),
            4: Timestamp("1961-05-01 00:00:00"),
            5: Timestamp("1961-06-01 00:00:00"),
            6: Timestamp("1961-07-01 00:00:00"),
            7: Timestamp("1961-08-01 00:00:00"),
            8: Timestamp("1961-09-01 00:00:00"),
            9: Timestamp("1961-10-01 00:00:00"),
            10: Timestamp("1961-11-01 00:00:00"),
            11: Timestamp("1961-12-01 00:00:00"),
            12: Timestamp("1962-01-01 00:00:00"),
            13: Timestamp("1962-02-01 00:00:00"),
            14: Timestamp("1962-03-01 00:00:00"),
        },
        "fcst": {
            0: 369.04567408561707,
            1: 361.5132256820798,
            2: 355.6997717395425,
            3: 351.53631968051195,
            4: 348.8190391138196,
            5: 346.9978071972728,
            6: 345.938841663301,
            7: 344.99859515577555,
            8: 344.5584729537368,
            9: 344.2660075649619,
            10: 344.0754380747676,
            11: 343.9527168497443,
            12: 343.8738499954343,
            13: 343.82346937805414,
            14: 343.79085744172335,
        },
        "fcst_lower": {
            0: 350.5933903813362,
            1: 343.4375643979758,
            2: 337.91478315256535,
            3: 333.95950369648637,
            4: 331.3780871581286,
            5: 329.6479168374091,
            6: 328.64189958013594,
            7: 327.74866539798677,
            8: 327.33054930604993,
            9: 327.0527071867138,
            10: 326.8716661710292,
            11: 326.7550810072571,
            12: 326.68015749566257,
            13: 326.6322959091514,
            14: 326.60131456963717,
        },
        "fcst_upper": {
            0: 387.49795778989795,
            1: 379.5888869661838,
            2: 373.4847603265196,
            3: 369.11313566453754,
            4: 366.2599910695106,
            5: 364.3476975571364,
            6: 363.23578374646604,
            7: 362.2485249135643,
            8: 361.78639660142363,
            9: 361.47930794321,
            10: 361.279209978506,
            11: 361.15035269223154,
            12: 361.067542495206,
            13: 361.01464284695686,
            14: 360.9804003138095,
        },
    }
)

AIR_FCST_30_LSTM_PARAM_1_MODEL_2_MONTHLY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("1961-01-01 00:00:00"),
            1: Timestamp("1961-02-01 00:00:00"),
            2: Timestamp("1961-03-01 00:00:00"),
            3: Timestamp("1961-04-01 00:00:00"),
            4: Timestamp("1961-05-01 00:00:00"),
            5: Timestamp("1961-06-01 00:00:00"),
            6: Timestamp("1961-07-01 00:00:00"),
            7: Timestamp("1961-08-01 00:00:00"),
            8: Timestamp("1961-09-01 00:00:00"),
            9: Timestamp("1961-10-01 00:00:00"),
            10: Timestamp("1961-11-01 00:00:00"),
            11: Timestamp("1961-12-01 00:00:00"),
            12: Timestamp("1962-01-01 00:00:00"),
            13: Timestamp("1962-02-01 00:00:00"),
            14: Timestamp("1962-03-01 00:00:00"),
            15: Timestamp("1962-04-01 00:00:00"),
            16: Timestamp("1962-05-01 00:00:00"),
            17: Timestamp("1962-06-01 00:00:00"),
            18: Timestamp("1962-07-01 00:00:00"),
            19: Timestamp("1962-08-01 00:00:00"),
            20: Timestamp("1962-09-01 00:00:00"),
            21: Timestamp("1962-10-01 00:00:00"),
            22: Timestamp("1962-11-01 00:00:00"),
            23: Timestamp("1962-12-01 00:00:00"),
            24: Timestamp("1963-01-01 00:00:00"),
            25: Timestamp("1963-02-01 00:00:00"),
            26: Timestamp("1963-03-01 00:00:00"),
            27: Timestamp("1963-04-01 00:00:00"),
            28: Timestamp("1963-05-01 00:00:00"),
            29: Timestamp("1963-06-01 00:00:00"),
        },
        "fcst": {
            0: 369.04567408561707,
            1: 361.5132256820798,
            2: 355.6997717395425,
            3: 351.53631968051195,
            4: 348.8190391138196,
            5: 346.9978071972728,
            6: 345.938841663301,
            7: 344.99859515577555,
            8: 344.5584729537368,
            9: 344.2660075649619,
            10: 344.0754380747676,
            11: 343.9527168497443,
            12: 343.8738499954343,
            13: 343.82346937805414,
            14: 343.79085744172335,
            15: 343.7707885578275,
            16: 343.7578711435199,
            17: 343.74960044771433,
            18: 343.74431692808867,
            19: 343.74095153063536,
            20: 343.7387979850173,
            21: 343.7374317571521,
            22: 343.73656725138426,
            23: 343.7360114976764,
            24: 343.73564871400595,
            25: 343.7354248687625,
            26: 343.7352859303355,
            27: 343.73518558591604,
            28: 343.73513155430555,
            29: 343.73509296029806,
        },
        "fcst_lower": {
            0: 350.5933903813362,
            1: 343.4375643979758,
            2: 337.91478315256535,
            3: 333.95950369648637,
            4: 331.3780871581286,
            5: 329.6479168374091,
            6: 328.64189958013594,
            7: 327.74866539798677,
            8: 327.33054930604993,
            9: 327.0527071867138,
            10: 326.8716661710292,
            11: 326.7550810072571,
            12: 326.68015749566257,
            13: 326.6322959091514,
            14: 326.60131456963717,
            15: 326.5822491299361,
            16: 326.5699775863439,
            17: 326.5621204253286,
            18: 326.5571010816842,
            19: 326.5539039541036,
            20: 326.5518580857664,
            21: 326.55056016929444,
            22: 326.54973888881506,
            23: 326.54921092279255,
            24: 326.54886627830564,
            25: 326.54865362532433,
            26: 326.5485216338187,
            27: 326.54842630662023,
            28: 326.54837497659025,
            29: 326.54833831228314,
        },
        "fcst_upper": {
            0: 387.49795778989795,
            1: 379.5888869661838,
            2: 373.4847603265196,
            3: 369.11313566453754,
            4: 366.2599910695106,
            5: 364.3476975571364,
            6: 363.23578374646604,
            7: 362.2485249135643,
            8: 361.78639660142363,
            9: 361.47930794321,
            10: 361.279209978506,
            11: 361.15035269223154,
            12: 361.067542495206,
            13: 361.01464284695686,
            14: 360.9804003138095,
            15: 360.95932798571886,
            16: 360.94576470069586,
            17: 360.93708047010006,
            18: 360.9315327744931,
            19: 360.9279991071671,
            20: 360.9257378842682,
            21: 360.9243033450097,
            22: 360.92339561395346,
            23: 360.9228120725602,
            24: 360.92243114970626,
            25: 360.92219611220065,
            26: 360.9220502268523,
            27: 360.92194486521186,
            28: 360.92188813202085,
            29: 360.921847608313,
        },
    }
)

AIR_FCST_15_LSTM_PARAM_2_MODEL_2_MONTHLY = pd.DataFrame(
    [
        {
            "time": Timestamp("1961-01-01 00:00:00"),
            "fcst": 410.1320439875126,
            "fcst_lower": 389.6254417881369,
            "fcst_upper": 430.63864618688825,
        },
        {
            "time": Timestamp("1961-02-01 00:00:00"),
            "fcst": 409.1636277139187,
            "fcst_lower": 388.7054463282227,
            "fcst_upper": 429.62180909961467,
        },
        {
            "time": Timestamp("1961-03-01 00:00:00"),
            "fcst": 409.22159591317177,
            "fcst_lower": 388.76051611751313,
            "fcst_upper": 429.6826757088304,
        },
        {
            "time": Timestamp("1961-04-01 00:00:00"),
            "fcst": 408.86590968072414,
            "fcst_lower": 388.4226141966879,
            "fcst_upper": 429.3092051647604,
        },
        {
            "time": Timestamp("1961-05-01 00:00:00"),
            "fcst": 408.83762799203396,
            "fcst_lower": 388.39574659243226,
            "fcst_upper": 429.27950939163566,
        },
        {
            "time": Timestamp("1961-06-01 00:00:00"),
            "fcst": 408.8285313844681,
            "fcst_lower": 388.38710481524464,
            "fcst_upper": 429.2699579536915,
        },
        {
            "time": Timestamp("1961-07-01 00:00:00"),
            "fcst": 408.8220321536064,
            "fcst_lower": 388.38093054592605,
            "fcst_upper": 429.2631337612868,
        },
        {
            "time": Timestamp("1961-08-01 00:00:00"),
            "fcst": 408.82110203802586,
            "fcst_lower": 388.38004693612453,
            "fcst_upper": 429.2621571399272,
        },
        {
            "time": Timestamp("1961-09-01 00:00:00"),
            "fcst": 408.8207701295614,
            "fcst_lower": 388.37973162308333,
            "fcst_upper": 429.2618086360395,
        },
        {
            "time": Timestamp("1961-10-01 00:00:00"),
            "fcst": 408.8206350505352,
            "fcst_lower": 388.3796032980084,
            "fcst_upper": 429.261666803062,
        },
        {
            "time": Timestamp("1961-11-01 00:00:00"),
            "fcst": 408.82060031592846,
            "fcst_lower": 388.379570300132,
            "fcst_upper": 429.2616303317249,
        },
        {
            "time": Timestamp("1961-12-01 00:00:00"),
            "fcst": 408.8205964565277,
            "fcst_lower": 388.3795666337013,
            "fcst_upper": 429.2616262793541,
        },
        {
            "time": Timestamp("1962-01-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-02-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-03-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
    ]
)

AIR_FCST_30_LSTM_PARAM_2_MODEL_2_MONTHLY = pd.DataFrame(
    [
        {
            "time": Timestamp("1961-01-01 00:00:00"),
            "fcst": 410.1320439875126,
            "fcst_lower": 389.6254417881369,
            "fcst_upper": 430.63864618688825,
        },
        {
            "time": Timestamp("1961-02-01 00:00:00"),
            "fcst": 409.1636277139187,
            "fcst_lower": 388.7054463282227,
            "fcst_upper": 429.62180909961467,
        },
        {
            "time": Timestamp("1961-03-01 00:00:00"),
            "fcst": 409.22159591317177,
            "fcst_lower": 388.76051611751313,
            "fcst_upper": 429.6826757088304,
        },
        {
            "time": Timestamp("1961-04-01 00:00:00"),
            "fcst": 408.86590968072414,
            "fcst_lower": 388.4226141966879,
            "fcst_upper": 429.3092051647604,
        },
        {
            "time": Timestamp("1961-05-01 00:00:00"),
            "fcst": 408.83762799203396,
            "fcst_lower": 388.39574659243226,
            "fcst_upper": 429.27950939163566,
        },
        {
            "time": Timestamp("1961-06-01 00:00:00"),
            "fcst": 408.8285313844681,
            "fcst_lower": 388.38710481524464,
            "fcst_upper": 429.2699579536915,
        },
        {
            "time": Timestamp("1961-07-01 00:00:00"),
            "fcst": 408.8220321536064,
            "fcst_lower": 388.38093054592605,
            "fcst_upper": 429.2631337612868,
        },
        {
            "time": Timestamp("1961-08-01 00:00:00"),
            "fcst": 408.82110203802586,
            "fcst_lower": 388.38004693612453,
            "fcst_upper": 429.2621571399272,
        },
        {
            "time": Timestamp("1961-09-01 00:00:00"),
            "fcst": 408.8207701295614,
            "fcst_lower": 388.37973162308333,
            "fcst_upper": 429.2618086360395,
        },
        {
            "time": Timestamp("1961-10-01 00:00:00"),
            "fcst": 408.8206350505352,
            "fcst_lower": 388.3796032980084,
            "fcst_upper": 429.261666803062,
        },
        {
            "time": Timestamp("1961-11-01 00:00:00"),
            "fcst": 408.82060031592846,
            "fcst_lower": 388.379570300132,
            "fcst_upper": 429.2616303317249,
        },
        {
            "time": Timestamp("1961-12-01 00:00:00"),
            "fcst": 408.8205964565277,
            "fcst_lower": 388.3795666337013,
            "fcst_upper": 429.2616262793541,
        },
        {
            "time": Timestamp("1962-01-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-02-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-03-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-04-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-05-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-06-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-07-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-08-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-09-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-10-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-11-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1962-12-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1963-01-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1963-02-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1963-03-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1963-04-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1963-05-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
        {
            "time": Timestamp("1963-06-01 00:00:00"),
            "fcst": 408.8205887377262,
            "fcst_lower": 388.3795593008399,
            "fcst_upper": 429.2616181746125,
        },
    ]
)

PT_FCST_15_LSTM_PARAM_1_MODEL_1_DAILY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("2013-05-01 00:00:00"),
            1: Timestamp("2013-05-02 00:00:00"),
            2: Timestamp("2013-05-03 00:00:00"),
            3: Timestamp("2013-05-04 00:00:00"),
            4: Timestamp("2013-05-05 00:00:00"),
            5: Timestamp("2013-05-06 00:00:00"),
            6: Timestamp("2013-05-07 00:00:00"),
            7: Timestamp("2013-05-08 00:00:00"),
            8: Timestamp("2013-05-09 00:00:00"),
            9: Timestamp("2013-05-10 00:00:00"),
            10: Timestamp("2013-05-11 00:00:00"),
            11: Timestamp("2013-05-12 00:00:00"),
            12: Timestamp("2013-05-13 00:00:00"),
            13: Timestamp("2013-05-14 00:00:00"),
            14: Timestamp("2013-05-15 00:00:00"),
        },
        "fcst": {
            0: 7.898664956802134,
            1: 7.865727279490495,
            2: 7.840503811852526,
            3: 7.815196089871443,
            4: 7.795936726853171,
            5: 7.781508074414368,
            6: 7.760805102354361,
            7: 7.746480325133095,
            8: 7.736601407222376,
            9: 7.72853671487028,
            10: 7.721865104602253,
            11: 7.716411014476557,
            12: 7.711931171298543,
            13: 7.708235137169395,
            14: 7.705322399125075,
        },
        "fcst_lower": {
            0: 7.503731708962027,
            1: 7.47244091551597,
            2: 7.448478621259899,
            3: 7.424436285377871,
            4: 7.406139890510512,
            5: 7.3924326706936485,
            6: 7.372764847236643,
            7: 7.35915630887644,
            8: 7.349771336861257,
            9: 7.342109879126766,
            10: 7.33577184937214,
            11: 7.330590463752729,
            12: 7.326334612733616,
            13: 7.322823380310925,
            14: 7.320056279168821,
        },
        "fcst_upper": {
            0: 8.29359820464224,
            1: 8.259013643465021,
            2: 8.232529002445153,
            3: 8.205955894365015,
            4: 8.18573356319583,
            5: 8.170583478135086,
            6: 8.14884535747208,
            7: 8.13380434138975,
            8: 8.123431477583495,
            9: 8.114963550613794,
            10: 8.107958359832367,
            11: 8.102231565200386,
            12: 8.09752772986347,
            13: 8.093646894027865,
            14: 8.09058851908133,
        },
    }
)

PT_FCST_15_LSTM_PARAM_2_MODEL_1_DAILY = pd.DataFrame(
    [
        {
            "time": Timestamp("2013-05-01 00:00:00"),
            "fcst": 7.868598852170829,
            "fcst_lower": 7.475168909562288,
            "fcst_upper": 8.262028794779372,
        },
        {
            "time": Timestamp("2013-05-02 00:00:00"),
            "fcst": 7.847685564855564,
            "fcst_lower": 7.4553012866127855,
            "fcst_upper": 8.240069843098343,
        },
        {
            "time": Timestamp("2013-05-03 00:00:00"),
            "fcst": 7.785592294078679,
            "fcst_lower": 7.396312679374745,
            "fcst_upper": 8.174871908782613,
        },
        {
            "time": Timestamp("2013-05-04 00:00:00"),
            "fcst": 7.74719975719529,
            "fcst_lower": 7.359839769335525,
            "fcst_upper": 8.134559745055055,
        },
        {
            "time": Timestamp("2013-05-05 00:00:00"),
            "fcst": 7.727431149129071,
            "fcst_lower": 7.341059591672617,
            "fcst_upper": 8.113802706585524,
        },
        {
            "time": Timestamp("2013-05-06 00:00:00"),
            "fcst": 7.710651582507708,
            "fcst_lower": 7.325119003382323,
            "fcst_upper": 8.096184161633094,
        },
        {
            "time": Timestamp("2013-05-07 00:00:00"),
            "fcst": 7.699919348921082,
            "fcst_lower": 7.314923381475027,
            "fcst_upper": 8.084915316367136,
        },
        {
            "time": Timestamp("2013-05-08 00:00:00"),
            "fcst": 7.693030754865313,
            "fcst_lower": 7.308379217122047,
            "fcst_upper": 8.077682292608579,
        },
        {
            "time": Timestamp("2013-05-09 00:00:00"),
            "fcst": 7.6881745243249915,
            "fcst_lower": 7.303765798108741,
            "fcst_upper": 8.072583250541241,
        },
        {
            "time": Timestamp("2013-05-10 00:00:00"),
            "fcst": 7.684973628732766,
            "fcst_lower": 7.300724947296127,
            "fcst_upper": 8.069222310169405,
        },
        {
            "time": Timestamp("2013-05-11 00:00:00"),
            "fcst": 7.682842776122172,
            "fcst_lower": 7.298700637316063,
            "fcst_upper": 8.06698491492828,
        },
        {
            "time": Timestamp("2013-05-12 00:00:00"),
            "fcst": 7.6814000647675025,
            "fcst_lower": 7.297330061529127,
            "fcst_upper": 8.065470068005878,
        },
        {
            "time": Timestamp("2013-05-13 00:00:00"),
            "fcst": 7.68043877016176,
            "fcst_lower": 7.296416831653671,
            "fcst_upper": 8.064460708669849,
        },
        {
            "time": Timestamp("2013-05-14 00:00:00"),
            "fcst": 7.679795513259092,
            "fcst_lower": 7.295805737596137,
            "fcst_upper": 8.063785288922046,
        },
        {
            "time": Timestamp("2013-05-15 00:00:00"),
            "fcst": 7.679363597539756,
            "fcst_lower": 7.295395417662768,
            "fcst_upper": 8.063331777416744,
        },
    ]
)

PT_FCST_15_LSTM_PARAM_1_MODEL_2_DAILY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("2013-05-01 00:00:00"),
            1: Timestamp("2013-05-02 00:00:00"),
            2: Timestamp("2013-05-03 00:00:00"),
            3: Timestamp("2013-05-04 00:00:00"),
            4: Timestamp("2013-05-05 00:00:00"),
            5: Timestamp("2013-05-06 00:00:00"),
            6: Timestamp("2013-05-07 00:00:00"),
            7: Timestamp("2013-05-08 00:00:00"),
            8: Timestamp("2013-05-09 00:00:00"),
            9: Timestamp("2013-05-10 00:00:00"),
            10: Timestamp("2013-05-11 00:00:00"),
            11: Timestamp("2013-05-12 00:00:00"),
            12: Timestamp("2013-05-13 00:00:00"),
            13: Timestamp("2013-05-14 00:00:00"),
            14: Timestamp("2013-05-15 00:00:00"),
        },
        "fcst": {
            0: 8.20442358323501,
            1: 8.2210381036774,
            2: 8.23673082774491,
            3: 8.247485759990184,
            4: 8.261724359253199,
            5: 8.278184349279377,
            6: 8.282260874483129,
            7: 8.29133802960368,
            8: 8.296318269199997,
            9: 8.30048328070017,
            10: 8.303923730497775,
            11: 8.306800432818482,
            12: 8.309085687604277,
            13: 8.310797063873439,
            14: 8.312255164149224,
        },
        "fcst_lower": {
            0: 7.79420240407326,
            1: 7.809986198493529,
            2: 7.824894286357664,
            3: 7.8351114719906745,
            4: 7.848638141290539,
            5: 7.864275131815408,
            6: 7.868147830758971,
            7: 7.876771128123495,
            8: 7.881502355739997,
            9: 7.885459116665162,
            10: 7.888727543972887,
            11: 7.891460411177558,
            12: 7.893631403224063,
            13: 7.895257210679767,
            14: 7.896642405941763,
        },
        "fcst_upper": {
            0: 8.614644762396761,
            1: 8.632090008861269,
            2: 8.648567369132156,
            3: 8.659860047989694,
            4: 8.67481057721586,
            5: 8.692093566743345,
            6: 8.696373918207286,
            7: 8.705904931083863,
            8: 8.711134182659997,
            9: 8.715507444735179,
            10: 8.719119917022665,
            11: 8.722140454459407,
            12: 8.724539971984491,
            13: 8.726336917067112,
            14: 8.727867922356687,
        },
    }
)

PT_FCST_15_LSTM_PARAM_2_MODEL_2_DAILY = pd.DataFrame(
    [
        {
            "time": Timestamp("2013-05-01 00:00:00"),
            "fcst": 8.397010031921214,
            "fcst_lower": 7.977159530325153,
            "fcst_upper": 8.816860533517275,
        },
        {
            "time": Timestamp("2013-05-02 00:00:00"),
            "fcst": 8.453978663725305,
            "fcst_lower": 8.031279730539039,
            "fcst_upper": 8.876677596911572,
        },
        {
            "time": Timestamp("2013-05-03 00:00:00"),
            "fcst": 8.471730938402239,
            "fcst_lower": 8.048144391482126,
            "fcst_upper": 8.895317485322352,
        },
        {
            "time": Timestamp("2013-05-04 00:00:00"),
            "fcst": 8.507812187574944,
            "fcst_lower": 8.082421578196197,
            "fcst_upper": 8.93320279695369,
        },
        {
            "time": Timestamp("2013-05-05 00:00:00"),
            "fcst": 8.518635600519184,
            "fcst_lower": 8.092703820493226,
            "fcst_upper": 8.944567380545143,
        },
        {
            "time": Timestamp("2013-05-06 00:00:00"),
            "fcst": 8.524949418371254,
            "fcst_lower": 8.09870194745269,
            "fcst_upper": 8.951196889289816,
        },
        {
            "time": Timestamp("2013-05-07 00:00:00"),
            "fcst": 8.529857201797315,
            "fcst_lower": 8.103364341707449,
            "fcst_upper": 8.95635006188718,
        },
        {
            "time": Timestamp("2013-05-08 00:00:00"),
            "fcst": 8.53198972154103,
            "fcst_lower": 8.105390235463979,
            "fcst_upper": 8.958589207618083,
        },
        {
            "time": Timestamp("2013-05-09 00:00:00"),
            "fcst": 8.533268540885809,
            "fcst_lower": 8.106605113841518,
            "fcst_upper": 8.9599319679301,
        },
        {
            "time": Timestamp("2013-05-10 00:00:00"),
            "fcst": 8.53405299113972,
            "fcst_lower": 8.107350341582734,
            "fcst_upper": 8.960755640696707,
        },
        {
            "time": Timestamp("2013-05-11 00:00:00"),
            "fcst": 8.534455667908935,
            "fcst_lower": 8.107732884513487,
            "fcst_upper": 8.961178451304383,
        },
        {
            "time": Timestamp("2013-05-12 00:00:00"),
            "fcst": 8.534690733678989,
            "fcst_lower": 8.107956196995039,
            "fcst_upper": 8.96142527036294,
        },
        {
            "time": Timestamp("2013-05-13 00:00:00"),
            "fcst": 8.534826156184813,
            "fcst_lower": 8.108084848375572,
            "fcst_upper": 8.961567463994054,
        },
        {
            "time": Timestamp("2013-05-14 00:00:00"),
            "fcst": 8.534900023006173,
            "fcst_lower": 8.108155021855865,
            "fcst_upper": 8.961645024156482,
        },
        {
            "time": Timestamp("2013-05-15 00:00:00"),
            "fcst": 8.534942342539244,
            "fcst_lower": 8.108195225412281,
            "fcst_upper": 8.961689459666207,
        },
    ]
)

PT_FCST_30_LSTM_PARAM_1_MODEL_1_DAILY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("2013-05-01 00:00:00"),
            1: Timestamp("2013-05-02 00:00:00"),
            2: Timestamp("2013-05-03 00:00:00"),
            3: Timestamp("2013-05-04 00:00:00"),
            4: Timestamp("2013-05-05 00:00:00"),
            5: Timestamp("2013-05-06 00:00:00"),
            6: Timestamp("2013-05-07 00:00:00"),
            7: Timestamp("2013-05-08 00:00:00"),
            8: Timestamp("2013-05-09 00:00:00"),
            9: Timestamp("2013-05-10 00:00:00"),
            10: Timestamp("2013-05-11 00:00:00"),
            11: Timestamp("2013-05-12 00:00:00"),
            12: Timestamp("2013-05-13 00:00:00"),
            13: Timestamp("2013-05-14 00:00:00"),
            14: Timestamp("2013-05-15 00:00:00"),
            15: Timestamp("2013-05-16 00:00:00"),
            16: Timestamp("2013-05-17 00:00:00"),
            17: Timestamp("2013-05-18 00:00:00"),
            18: Timestamp("2013-05-19 00:00:00"),
            19: Timestamp("2013-05-20 00:00:00"),
            20: Timestamp("2013-05-21 00:00:00"),
            21: Timestamp("2013-05-22 00:00:00"),
            22: Timestamp("2013-05-23 00:00:00"),
            23: Timestamp("2013-05-24 00:00:00"),
            24: Timestamp("2013-05-25 00:00:00"),
            25: Timestamp("2013-05-26 00:00:00"),
            26: Timestamp("2013-05-27 00:00:00"),
            27: Timestamp("2013-05-28 00:00:00"),
            28: Timestamp("2013-05-29 00:00:00"),
            29: Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 7.898664956802134,
            1: 7.865727279490495,
            2: 7.840503811852526,
            3: 7.815196089871443,
            4: 7.795936726853171,
            5: 7.781508074414368,
            6: 7.760805102354361,
            7: 7.746480325133095,
            8: 7.736601407222376,
            9: 7.72853671487028,
            10: 7.721865104602253,
            11: 7.716411014476557,
            12: 7.711931171298543,
            13: 7.708235137169395,
            14: 7.705322399125075,
            15: 7.7029989285185065,
            16: 7.7011190435631205,
            17: 7.699597976951766,
            18: 7.698369556323644,
            19: 7.697378125080715,
            20: 7.696579696556789,
            21: 7.695937978546232,
            22: 7.695421167278737,
            23: 7.695004896962537,
            24: 7.69466916200018,
            25: 7.694398829952567,
            26: 7.694181076718769,
            27: 7.694005643018041,
            28: 7.6938645779078065,
            29: 7.693750828132554,
        },
        "fcst_lower": {
            0: 7.503731708962027,
            1: 7.47244091551597,
            2: 7.448478621259899,
            3: 7.424436285377871,
            4: 7.406139890510512,
            5: 7.3924326706936485,
            6: 7.372764847236643,
            7: 7.35915630887644,
            8: 7.349771336861257,
            9: 7.342109879126766,
            10: 7.33577184937214,
            11: 7.330590463752729,
            12: 7.326334612733616,
            13: 7.322823380310925,
            14: 7.320056279168821,
            15: 7.317848982092581,
            16: 7.316063091384964,
            17: 7.314618078104178,
            18: 7.313451078507461,
            19: 7.312509218826679,
            20: 7.3117507117289495,
            21: 7.3111410796189205,
            22: 7.3106501089148,
            23: 7.31025465211441,
            24: 7.3099357039001704,
            25: 7.309678888454938,
            26: 7.30947202288283,
            27: 7.309305360867139,
            28: 7.3091713490124155,
            29: 7.309063286725926,
        },
        "fcst_upper": {
            0: 8.29359820464224,
            1: 8.259013643465021,
            2: 8.232529002445153,
            3: 8.205955894365015,
            4: 8.18573356319583,
            5: 8.170583478135086,
            6: 8.14884535747208,
            7: 8.13380434138975,
            8: 8.123431477583495,
            9: 8.114963550613794,
            10: 8.107958359832367,
            11: 8.102231565200386,
            12: 8.09752772986347,
            13: 8.093646894027865,
            14: 8.09058851908133,
            15: 8.088148874944432,
            16: 8.086174995741278,
            17: 8.084577875799354,
            18: 8.083288034139827,
            19: 8.08224703133475,
            20: 8.081408681384628,
            21: 8.080734877473544,
            22: 8.080192225642675,
            23: 8.079755141810665,
            24: 8.07940262010019,
            25: 8.079118771450196,
            26: 8.078890130554708,
            27: 8.078705925168943,
            28: 8.078557806803197,
            29: 8.078438369539182,
        },
    }
)

PT_FCST_30_LSTM_PARAM_2_MODEL_1_DAILY = pd.DataFrame(
    [
        {
            "time": Timestamp("2013-05-01 00:00:00"),
            "fcst": 7.868598852170829,
            "fcst_lower": 7.475168909562288,
            "fcst_upper": 8.262028794779372,
        },
        {
            "time": Timestamp("2013-05-02 00:00:00"),
            "fcst": 7.847685564855564,
            "fcst_lower": 7.4553012866127855,
            "fcst_upper": 8.240069843098343,
        },
        {
            "time": Timestamp("2013-05-03 00:00:00"),
            "fcst": 7.785592294078679,
            "fcst_lower": 7.396312679374745,
            "fcst_upper": 8.174871908782613,
        },
        {
            "time": Timestamp("2013-05-04 00:00:00"),
            "fcst": 7.74719975719529,
            "fcst_lower": 7.359839769335525,
            "fcst_upper": 8.134559745055055,
        },
        {
            "time": Timestamp("2013-05-05 00:00:00"),
            "fcst": 7.727431149129071,
            "fcst_lower": 7.341059591672617,
            "fcst_upper": 8.113802706585524,
        },
        {
            "time": Timestamp("2013-05-06 00:00:00"),
            "fcst": 7.710651582507708,
            "fcst_lower": 7.325119003382323,
            "fcst_upper": 8.096184161633094,
        },
        {
            "time": Timestamp("2013-05-07 00:00:00"),
            "fcst": 7.699919348921082,
            "fcst_lower": 7.314923381475027,
            "fcst_upper": 8.084915316367136,
        },
        {
            "time": Timestamp("2013-05-08 00:00:00"),
            "fcst": 7.693030754865313,
            "fcst_lower": 7.308379217122047,
            "fcst_upper": 8.077682292608579,
        },
        {
            "time": Timestamp("2013-05-09 00:00:00"),
            "fcst": 7.6881745243249915,
            "fcst_lower": 7.303765798108741,
            "fcst_upper": 8.072583250541241,
        },
        {
            "time": Timestamp("2013-05-10 00:00:00"),
            "fcst": 7.684973628732766,
            "fcst_lower": 7.300724947296127,
            "fcst_upper": 8.069222310169405,
        },
        {
            "time": Timestamp("2013-05-11 00:00:00"),
            "fcst": 7.682842776122172,
            "fcst_lower": 7.298700637316063,
            "fcst_upper": 8.06698491492828,
        },
        {
            "time": Timestamp("2013-05-12 00:00:00"),
            "fcst": 7.6814000647675025,
            "fcst_lower": 7.297330061529127,
            "fcst_upper": 8.065470068005878,
        },
        {
            "time": Timestamp("2013-05-13 00:00:00"),
            "fcst": 7.68043877016176,
            "fcst_lower": 7.296416831653671,
            "fcst_upper": 8.064460708669849,
        },
        {
            "time": Timestamp("2013-05-14 00:00:00"),
            "fcst": 7.679795513259092,
            "fcst_lower": 7.295805737596137,
            "fcst_upper": 8.063785288922046,
        },
        {
            "time": Timestamp("2013-05-15 00:00:00"),
            "fcst": 7.679363597539756,
            "fcst_lower": 7.295395417662768,
            "fcst_upper": 8.063331777416744,
        },
        {
            "time": Timestamp("2013-05-16 00:00:00"),
            "fcst": 7.6790750552688225,
            "fcst_lower": 7.295121302505381,
            "fcst_upper": 8.063028808032264,
        },
        {
            "time": Timestamp("2013-05-17 00:00:00"),
            "fcst": 7.6788819243088104,
            "fcst_lower": 7.29493782809337,
            "fcst_upper": 8.062826020524252,
        },
        {
            "time": Timestamp("2013-05-18 00:00:00"),
            "fcst": 7.6787524008894135,
            "fcst_lower": 7.294814780844942,
            "fcst_upper": 8.062690020933884,
        },
        {
            "time": Timestamp("2013-05-19 00:00:00"),
            "fcst": 7.678665966449143,
            "fcst_lower": 7.294732668126685,
            "fcst_upper": 8.0625992647716,
        },
        {
            "time": Timestamp("2013-05-20 00:00:00"),
            "fcst": 7.678607745030919,
            "fcst_lower": 7.294677357779372,
            "fcst_upper": 8.062538132282466,
        },
        {
            "time": Timestamp("2013-05-21 00:00:00"),
            "fcst": 7.678569016246109,
            "fcst_lower": 7.294640565433803,
            "fcst_upper": 8.062497467058416,
        },
        {
            "time": Timestamp("2013-05-22 00:00:00"),
            "fcst": 7.67854311156223,
            "fcst_lower": 7.294615955984118,
            "fcst_upper": 8.062470267140341,
        },
        {
            "time": Timestamp("2013-05-23 00:00:00"),
            "fcst": 7.678525670784964,
            "fcst_lower": 7.294599387245715,
            "fcst_upper": 8.062451954324212,
        },
        {
            "time": Timestamp("2013-05-24 00:00:00"),
            "fcst": 7.6785138726121085,
            "fcst_lower": 7.294588178981503,
            "fcst_upper": 8.062439566242714,
        },
        {
            "time": Timestamp("2013-05-25 00:00:00"),
            "fcst": 7.67850617815155,
            "fcst_lower": 7.294580869243972,
            "fcst_upper": 8.062431487059127,
        },
        {
            "time": Timestamp("2013-05-26 00:00:00"),
            "fcst": 7.678501048511178,
            "fcst_lower": 7.2945759960856185,
            "fcst_upper": 8.062426100936737,
        },
        {
            "time": Timestamp("2013-05-27 00:00:00"),
            "fcst": 7.6784974577629175,
            "fcst_lower": 7.294572584874771,
            "fcst_upper": 8.062422330651064,
        },
        {
            "time": Timestamp("2013-05-28 00:00:00"),
            "fcst": 7.6784954059067685,
            "fcst_lower": 7.2945706356114295,
            "fcst_upper": 8.062420176202107,
        },
        {
            "time": Timestamp("2013-05-29 00:00:00"),
            "fcst": 7.678493867014657,
            "fcst_lower": 7.294569173663924,
            "fcst_upper": 8.06241856036539,
        },
        {
            "time": Timestamp("2013-05-30 00:00:00"),
            "fcst": 7.678492584604564,
            "fcst_lower": 7.294567955374336,
            "fcst_upper": 8.062417213834793,
        },
    ]
)

PT_FCST_30_LSTM_PARAM_1_MODEL_2_DAILY = pd.DataFrame(
    {
        "time": {
            0: Timestamp("2013-05-01 00:00:00"),
            1: Timestamp("2013-05-02 00:00:00"),
            2: Timestamp("2013-05-03 00:00:00"),
            3: Timestamp("2013-05-04 00:00:00"),
            4: Timestamp("2013-05-05 00:00:00"),
            5: Timestamp("2013-05-06 00:00:00"),
            6: Timestamp("2013-05-07 00:00:00"),
            7: Timestamp("2013-05-08 00:00:00"),
            8: Timestamp("2013-05-09 00:00:00"),
            9: Timestamp("2013-05-10 00:00:00"),
            10: Timestamp("2013-05-11 00:00:00"),
            11: Timestamp("2013-05-12 00:00:00"),
            12: Timestamp("2013-05-13 00:00:00"),
            13: Timestamp("2013-05-14 00:00:00"),
            14: Timestamp("2013-05-15 00:00:00"),
            15: Timestamp("2013-05-16 00:00:00"),
            16: Timestamp("2013-05-17 00:00:00"),
            17: Timestamp("2013-05-18 00:00:00"),
            18: Timestamp("2013-05-19 00:00:00"),
            19: Timestamp("2013-05-20 00:00:00"),
            20: Timestamp("2013-05-21 00:00:00"),
            21: Timestamp("2013-05-22 00:00:00"),
            22: Timestamp("2013-05-23 00:00:00"),
            23: Timestamp("2013-05-24 00:00:00"),
            24: Timestamp("2013-05-25 00:00:00"),
            25: Timestamp("2013-05-26 00:00:00"),
            26: Timestamp("2013-05-27 00:00:00"),
            27: Timestamp("2013-05-28 00:00:00"),
            28: Timestamp("2013-05-29 00:00:00"),
            29: Timestamp("2013-05-30 00:00:00"),
        },
        "fcst": {
            0: 8.20442358323501,
            1: 8.2210381036774,
            2: 8.23673082774491,
            3: 8.247485759990184,
            4: 8.261724359253199,
            5: 8.278184349279377,
            6: 8.282260874483129,
            7: 8.29133802960368,
            8: 8.296318269199997,
            9: 8.30048328070017,
            10: 8.303923730497775,
            11: 8.306800432818482,
            12: 8.309085687604277,
            13: 8.310797063873439,
            14: 8.312255164149224,
            15: 8.31337599057054,
            16: 8.314281243855216,
            17: 8.315009139824026,
            18: 8.315591995211312,
            19: 8.316056227664992,
            20: 8.316426202976833,
            21: 8.316723337395391,
            22: 8.316960070298567,
            23: 8.317149738751327,
            24: 8.317301319624324,
            25: 8.317422635619126,
            26: 8.31751958582216,
            27: 8.31759691515077,
            28: 8.317658855558264,
            29: 8.317708356587854,
        },
        "fcst_lower": {
            0: 7.79420240407326,
            1: 7.809986198493529,
            2: 7.824894286357664,
            3: 7.8351114719906745,
            4: 7.848638141290539,
            5: 7.864275131815408,
            6: 7.868147830758971,
            7: 7.876771128123495,
            8: 7.881502355739997,
            9: 7.885459116665162,
            10: 7.888727543972887,
            11: 7.891460411177558,
            12: 7.893631403224063,
            13: 7.895257210679767,
            14: 7.896642405941763,
            15: 7.897707191042013,
            16: 7.898567181662455,
            17: 7.899258682832825,
            18: 7.899812395450746,
            19: 7.900253416281743,
            20: 7.9006048928279915,
            21: 7.900887170525621,
            22: 7.9011120667836385,
            23: 7.901292251813761,
            24: 7.901436253643108,
            25: 7.901551503838169,
            26: 7.901643606531051,
            27: 7.90171706939323,
            28: 7.901775912780351,
            29: 7.901822938758461,
        },
        "fcst_upper": {
            0: 8.614644762396761,
            1: 8.632090008861269,
            2: 8.648567369132156,
            3: 8.659860047989694,
            4: 8.67481057721586,
            5: 8.692093566743345,
            6: 8.696373918207286,
            7: 8.705904931083863,
            8: 8.711134182659997,
            9: 8.715507444735179,
            10: 8.719119917022665,
            11: 8.722140454459407,
            12: 8.724539971984491,
            13: 8.726336917067112,
            14: 8.727867922356687,
            15: 8.729044790099067,
            16: 8.729995306047977,
            17: 8.730759596815227,
            18: 8.731371594971877,
            19: 8.731859039048242,
            20: 8.732247513125676,
            21: 8.732559504265161,
            22: 8.732808073813496,
            23: 8.733007225688894,
            24: 8.733166385605541,
            25: 8.733293767400083,
            26: 8.733395565113268,
            27: 8.733476760908308,
            28: 8.733541798336178,
            29: 8.733593774417248,
        },
    }
)

PT_FCST_30_LSTM_PARAM_2_MODEL_2_DAILY = pd.DataFrame(
    [
        {
            "time": Timestamp("2013-05-01 00:00:00"),
            "fcst": 8.397010031921214,
            "fcst_lower": 7.977159530325153,
            "fcst_upper": 8.816860533517275,
        },
        {
            "time": Timestamp("2013-05-02 00:00:00"),
            "fcst": 8.453978663725305,
            "fcst_lower": 8.031279730539039,
            "fcst_upper": 8.876677596911572,
        },
        {
            "time": Timestamp("2013-05-03 00:00:00"),
            "fcst": 8.471730938402239,
            "fcst_lower": 8.048144391482126,
            "fcst_upper": 8.895317485322352,
        },
        {
            "time": Timestamp("2013-05-04 00:00:00"),
            "fcst": 8.507812187574944,
            "fcst_lower": 8.082421578196197,
            "fcst_upper": 8.93320279695369,
        },
        {
            "time": Timestamp("2013-05-05 00:00:00"),
            "fcst": 8.518635600519184,
            "fcst_lower": 8.092703820493226,
            "fcst_upper": 8.944567380545143,
        },
        {
            "time": Timestamp("2013-05-06 00:00:00"),
            "fcst": 8.524949418371254,
            "fcst_lower": 8.09870194745269,
            "fcst_upper": 8.951196889289816,
        },
        {
            "time": Timestamp("2013-05-07 00:00:00"),
            "fcst": 8.529857201797315,
            "fcst_lower": 8.103364341707449,
            "fcst_upper": 8.95635006188718,
        },
        {
            "time": Timestamp("2013-05-08 00:00:00"),
            "fcst": 8.53198972154103,
            "fcst_lower": 8.105390235463979,
            "fcst_upper": 8.958589207618083,
        },
        {
            "time": Timestamp("2013-05-09 00:00:00"),
            "fcst": 8.533268540885809,
            "fcst_lower": 8.106605113841518,
            "fcst_upper": 8.9599319679301,
        },
        {
            "time": Timestamp("2013-05-10 00:00:00"),
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